Meeting Title: ABC | Planning Date: 2025-07-14 Meeting participants: Amber Lin, Brainforge, Mustafa Raja, Luke Daque, Casie Aviles, Annie Yu


WEBVTT

1 00:00:34.160 00:00:34.850 Mustafa Raja: Hey!

2 00:00:36.360 00:00:37.400 Amber Lin: Hello,

3 00:00:40.100 00:00:44.059 Amber Lin: Oh, let me ask Annie and Luke if they’re gonna join.

4 00:00:44.630 00:00:45.440 Mustafa Raja: Yep.

5 00:01:03.030 00:01:03.580 Amber Lin: Okay.

6 00:01:04.860 00:01:06.030 Amber Lin: Oh, Hi, Luke!

7 00:01:06.290 00:01:07.349 Amber Lin: There we go!

8 00:01:07.700 00:01:08.200 Luke Daque: I got it.

9 00:01:08.200 00:01:09.270 Amber Lin: Hi, Casey.

10 00:01:10.597 00:01:14.479 Amber Lin: So let me share my screen.

11 00:01:14.730 00:01:18.129 Amber Lin: We’re going to do a planning session today.

12 00:01:18.800 00:01:24.710 Amber Lin: I haven’t yet put anything into the cycle. These are all that kind of carried over from last time.

13 00:01:25.890 00:01:29.309 Amber Lin: So let’s go to the projects.

14 00:01:29.730 00:01:33.949 Amber Lin: And I think for

15 00:01:39.190 00:01:47.919 Amber Lin: yeah. Okay. So, Luke, I know. I think most of your work is done with the with the dashboards right?

16 00:01:48.190 00:01:53.210 Amber Lin: Is there still any ae support that Annie would need for the dashboards?

17 00:01:55.565 00:01:59.909 Luke Daque: I think there’s 1. We had a call last Friday, and.

18 00:02:00.390 00:02:04.469 Luke Daque: Like. She wanted me to review one of the models that she created in.

19 00:02:04.470 00:02:05.010 Amber Lin: Hmm.

20 00:02:06.420 00:02:14.280 Luke Daque: Yeah, for ABC, basically, which was like joining the 8 by 8, I mean, wait, I think I’m.

21 00:02:14.690 00:02:17.600 Amber Lin: Yeah, I remember. That was join

22 00:02:17.600 00:02:36.079 Amber Lin: models. Yeah. 8 by 8. And bot data. Join models. And I know we wanted to move them to from real to say, dbt, do we have dbt set up for ABC.

23 00:02:36.080 00:02:41.460 Luke Daque: Oh, I didn’t know if that oh, then we were like moving.

24 00:02:41.670 00:02:42.310 Amber Lin: Oh, I mean.

25 00:02:42.310 00:02:43.280 Luke Daque: Through those.

26 00:02:43.280 00:02:44.639 Amber Lin: Could? What do you think?

27 00:02:45.690 00:02:50.450 Luke Daque: I don’t know. I’ll we’ll have to take a look. But yeah, we can do that. We can

28 00:02:51.270 00:02:54.000 Luke Daque: move that to Dbt. I’m not sure if there’s

29 00:02:54.560 00:02:57.240 Luke Daque: dbt already for ABC. Let me check.

30 00:02:57.574 00:03:04.260 Amber Lin: I don’t think so, cause we never really had an ae unless a wave made one. Then it doesn’t exist.

31 00:03:06.800 00:03:14.064 Luke Daque: yeah, I don’t think there is. I just looked at the repository, and it looks like there’s what you think for ABC.

32 00:03:14.750 00:03:21.720 Amber Lin: Okay. So I’m just gonna say, what creates

33 00:03:22.140 00:03:26.380 Amber Lin: set up? Dvt for? How long would it take to set up? Dbt.

34 00:03:28.540 00:03:31.599 Luke Daque: It shouldn’t take too long, like maybe

35 00:03:33.940 00:03:36.870 Luke Daque: just setting up without creating the models. Right? You mean.

36 00:03:38.180 00:03:42.760 Luke Daque: Yeah, that should be like 2 h or something up to 2 h.

37 00:03:43.330 00:03:45.687 Amber Lin: Okay. Awesome. I’m gonna say, that

38 00:03:46.320 00:03:50.700 Amber Lin: set up Dvt, and then I’m gonna say, transfer

39 00:03:51.808 00:03:56.110 Amber Lin: for reviews. You’re just giving her comments on like best practices right?

40 00:03:57.970 00:04:06.370 Luke Daque: But it’s yeah. It’s basically like checking the models that Annie created. And like seeing if there’s anything wrong or yeah, something like that.

41 00:04:06.370 00:04:07.100 Amber Lin: Okay.

42 00:04:08.280 00:04:11.550 Amber Lin: Okay. I’m gonna say, this

43 00:04:11.840 00:04:21.319 Amber Lin: ticket. How long would that take? I don’t think she did a heavy it. It wasn’t very heavy, but I know, like setting this model up might take some time.

44 00:04:22.300 00:04:24.100 Luke Daque: It shouldn’t be pretty easy.

45 00:04:24.850 00:04:26.340 Luke Daque: It shouldn’t take long, so.

46 00:04:26.340 00:04:27.150 Amber Lin: So like.

47 00:04:27.150 00:04:28.439 Luke Daque: 2. Point. Yeah.

48 00:04:28.440 00:04:33.150 Amber Lin: Oh, okay, okay, so these are

49 00:04:33.440 00:04:37.230 Amber Lin: that was like transferring the real right? Yeah.

50 00:04:37.580 00:04:41.939 Amber Lin: So I was wondering if that’s still 1 point or like 2 2 points.

51 00:04:42.260 00:04:46.839 Luke Daque: Let me see how many models we have, because I think there were a couple already.

52 00:04:49.780 00:04:58.719 Luke Daque: There’s 3. There’s 4, 4, yeah, 4 models. Let’s make that like 2 points. I guess.

53 00:04:58.720 00:04:59.420 Amber Lin: Okay.

54 00:05:00.323 00:05:03.960 Amber Lin: The review join models probably would. Wouldn’t take that long.

55 00:05:03.960 00:05:04.320 Luke Daque: Yeah.

56 00:05:04.320 00:05:05.220 Amber Lin: I’m just gonna say.

57 00:05:05.220 00:05:05.800 Luke Daque: Don’t want to.

58 00:05:05.800 00:05:09.479 Amber Lin: Yeah, and then dashboards. See.

59 00:05:09.970 00:05:16.270 Amber Lin: Hi, Annie, how long would you estimate the dashboards would take if Luke Helps set, set up the modeling.

60 00:05:18.500 00:05:25.160 Annie Yu: It shouldn’t take too long if we’re just replicating. But right now the issues that we have is.

61 00:05:25.550 00:05:28.080 Annie Yu: Won’t have this time stamp, so we can’t do the jump.

62 00:05:28.463 00:05:31.150 Amber Lin: Okay, let me let me get that

63 00:05:31.410 00:05:35.890 Amber Lin: Timestamps data from 8 by 8.

64 00:05:36.260 00:05:39.359 Amber Lin: Okay, so that’s urgent.

65 00:05:40.040 00:05:42.479 Amber Lin: I’m going to assign it to

66 00:05:45.950 00:05:51.889 Amber Lin: I’ll put it on, Luke for now. But I think, Casey, you had some.

67 00:05:52.170 00:05:56.740 Amber Lin: You had some comments earlier, so.

68 00:05:56.740 00:06:06.810 Luke Daque: Yeah to the Timestamps thing when we looked at it with Annie last Friday, I think that was in the source in the source itself.

69 00:06:07.600 00:06:20.149 Amber Lin: Yeah, I checked in. There was like the Doc original document that from the Api source there wasn’t really a timestamp. So I don’t think that endpoint gets us the timestamps

70 00:06:21.582 00:06:23.709 Amber Lin: oh, maybe this one

71 00:06:27.690 00:06:30.740 Amber Lin: think this this Api might give you the timestamp.

72 00:06:34.590 00:06:38.789 Amber Lin: And okay, I think this will work

73 00:06:40.250 00:06:44.810 Amber Lin: it, says Timestamps. Here we I think we already, Casey. We already have.

74 00:06:45.250 00:06:47.489 Amber Lin: Well, the Api for this one right.

75 00:06:48.935 00:06:56.179 Casie Aviles: Yeah, we were able to access this one previously, although I do think that any has taken a look at this. And

76 00:06:57.087 00:07:01.059 Casie Aviles: the I think there was a mismatch with the like.

77 00:07:01.270 00:07:04.800 Casie Aviles: The the contents of this one right.

78 00:07:07.300 00:07:10.939 Annie Yu: Yeah, I need to take a moment to to look through that. I can’t think.

79 00:07:11.410 00:07:12.400 Annie Yu: Very very cold.

80 00:07:12.400 00:07:15.199 Amber Lin: Yeah. It’s a long time ago, so don’t don’t worry.

81 00:07:15.200 00:07:17.720 Annie Yu: I do remember. Yeah, that 1st version.

82 00:07:17.720 00:07:19.080 Casie Aviles: Did link the thread. Sorry.

83 00:07:19.726 00:07:21.019 Annie Yu: Was not.

84 00:07:21.160 00:07:21.845 Annie Yu: Yeah.

85 00:07:24.340 00:07:25.060 Amber Lin: Okay.

86 00:07:25.570 00:07:38.900 Amber Lin: So let’s say, we’re gonna look@api.documentation determine if they have they.

87 00:07:39.320 00:07:42.660 Amber Lin: We need timestamps

88 00:07:44.190 00:08:01.709 Amber Lin: for the right granularity. I can’t spell, but I can’t. I think that’s that was our problem originally. So we’ll have to check maybe these, this and the other Api can be joined but I think we’ll have to investigate that.

89 00:08:04.530 00:08:09.899 Amber Lin: Okay, so, Spike, I’ll say that.

90 00:08:10.120 00:08:12.640 Amber Lin: Who’s going to be checking the data.

91 00:08:23.176 00:08:25.840 Casie Aviles: Sorry like what exactly they got.

92 00:08:26.370 00:08:26.900 Casie Aviles: Contents of.

93 00:08:26.900 00:08:32.130 Amber Lin: Checking the api documentation. If they have the Timestamps, we need.

94 00:08:34.230 00:08:42.740 Casie Aviles: I mean, I I can help with the checking it up. But I would need like confirmation on the data side, like, if that’s what is needed.

95 00:08:42.740 00:08:49.479 Amber Lin: Hmm! I mean, Luke, would it be easier if you look at it because you have context with, since you already talked with Annie about it?

96 00:08:50.710 00:08:53.230 Luke Daque: I think that’s essentially what Annie needed.

97 00:08:53.590 00:08:54.370 Amber Lin: Oh, okay.

98 00:08:54.370 00:08:58.030 Luke Daque: So, yeah, I think, yeah, if we can pull that instead of the current

99 00:08:59.580 00:09:02.820 Luke Daque: one that we have, which was just the date.

100 00:09:03.070 00:09:03.850 Amber Lin: Yeah.

101 00:09:03.850 00:09:04.550 Luke Daque: I think today.

102 00:09:04.550 00:09:16.640 Amber Lin: You might have to check the documentation on this site to see if they where or what Api has that level of granularity? I signed it

103 00:09:17.410 00:09:23.549 Amber Lin: to you, and maybe we could. We could prioritize this one.

104 00:09:25.720 00:09:33.670 Amber Lin: I’ll say tomorrow, cause we just need to look at the Api documentations. Just let us know if the current stuff we have

105 00:09:35.350 00:09:45.750 Amber Lin: if the current Apis we have will provide the data check with Casey on

106 00:09:46.210 00:09:48.930 Amber Lin: what Apis we do have.

107 00:09:49.430 00:09:51.029 Amber Lin: I’m just going to save that

108 00:09:51.802 00:09:55.879 Amber Lin: and then the other ones. I’ll assign

109 00:09:57.830 00:10:05.500 Amber Lin: due dates later, Annie, for this one. You’re you’re blocked until you get the Timestamps right.

110 00:10:06.380 00:10:07.130 Annie Yu: Yes.

111 00:10:08.016 00:10:13.559 Annie Yu: I looked through the new data that we got, and I have a Pr open. I just tried.

112 00:10:13.560 00:10:13.970 Amber Lin: I don’t.

113 00:10:13.970 00:10:20.274 Annie Yu: Some prepping work because we data that we didn’t have. And I just tried to prep them in the

114 00:10:20.850 00:10:24.340 Annie Yu: right formats. But that’s all I can do so far.

115 00:10:24.340 00:10:28.740 Amber Lin: Okay, okay, sounds good. So that’s Npr, review.

116 00:10:29.660 00:10:30.210 Annie Yu: Oh!

117 00:10:30.210 00:10:31.000 Amber Lin: From you.

118 00:10:31.000 00:10:33.441 Annie Yu: Hi Luke as the reviewer.

119 00:10:34.120 00:10:36.200 Annie Yu: so that might take a while.

120 00:10:36.710 00:10:42.040 Amber Lin: Okay. I mean, you’re you’re blocked. If it gets reviewed there, there’s nothing we can show the client.

121 00:10:42.375 00:10:42.710 Annie Yu: Good.

122 00:10:42.710 00:10:44.550 Amber Lin: So it’s it’s okay.

123 00:10:45.145 00:10:56.100 Amber Lin: I’m gonna say, these 2 dashboards. I’ll just say end of the cycle. We’ll adjust to do this later view. Join models. I’ll just say, end of this week.

124 00:10:56.230 00:11:01.980 Amber Lin: Okay? And then the just the spike. We we need it urgently. Other than that. We’re fine.

125 00:11:02.250 00:11:10.029 Amber Lin: Okay, and, Annie, I will. I will help define the tickets for the dashboard. But since it’s not that urgent, I have a little bit more time.

126 00:11:10.910 00:11:11.620 Annie Yu: Awesome.

127 00:11:11.790 00:11:12.490 Amber Lin: Okay,

128 00:11:14.400 00:11:24.670 Amber Lin: So, looking at this, I think Luke and a feel free to hop, the the rest is not related to data work. It’s all like AI and documentation and stuff.

129 00:11:26.020 00:11:27.599 Annie Yu: Okay. Thank you guys.

130 00:11:27.600 00:11:27.960 Amber Lin: Okay.

131 00:11:27.960 00:11:28.780 Luke Daque: And thanks everyone.

132 00:11:28.780 00:11:29.170 Amber Lin: Thanks you too.

133 00:11:29.170 00:11:29.840 Luke Daque: Bye.

134 00:11:32.440 00:11:41.063 Amber Lin: Okay, so this is my core team of engineers.

135 00:11:41.820 00:11:43.150 Mustafa Raja: Oh!

136 00:11:44.290 00:11:54.660 Amber Lin: How was the sorry? I I’m just trying to wrap my head around stuff. So for the knowledge base, we have the central Doc, and then we have the spreadsheets.

137 00:11:54.990 00:11:57.359 Amber Lin: I think a priority.

138 00:11:57.770 00:12:00.739 Amber Lin: This sprint is to make sure that

139 00:12:00.870 00:12:12.860 Amber Lin: we have the spreadsheets integrated and working, and so

140 00:12:14.120 00:12:17.590 Amber Lin: I think for the inspector sheet at this point. It’s just

141 00:12:19.190 00:12:23.110 Amber Lin: We’re gonna test, hey?

142 00:12:23.700 00:12:26.789 Amber Lin: To confirm. This is ready for the client to use right.

143 00:12:28.453 00:12:28.946 Casie Aviles: Yes,

144 00:12:29.440 00:12:29.980 Amber Lin: Okay?

145 00:12:30.530 00:12:42.429 Amber Lin: Oh, good. I I think the client will have feedback, because then, if their original sheet is not correct, our results are not going to be correct. I’ve got a few feedback from them, but I think it’s just more feedback that comes in.

146 00:12:42.560 00:12:46.169 Amber Lin: It will change this. Let me.

147 00:12:46.550 00:12:47.910 Casie Aviles: Let’s see.

148 00:12:48.610 00:12:52.269 Amber Lin: Yeah, I’m gonna move that into cycle.

149 00:12:53.930 00:12:55.849 Amber Lin: I can do.

150 00:12:56.690 00:13:01.410 Amber Lin: I can do that one high priority.

151 00:13:01.980 00:13:10.160 Amber Lin: Say, end of this cycle and then coverage tracker.

152 00:13:11.340 00:13:12.080 Amber Lin: Oh.

153 00:13:17.650 00:13:18.660 Amber Lin: yeah.

154 00:13:19.030 00:13:24.689 Amber Lin: So think the central dock ones. I’ll put under me.

155 00:13:24.860 00:13:29.030 Amber Lin: And then, 4, th this cycle.

156 00:13:29.460 00:13:43.189 Amber Lin: How does the other 2 spreadsheets look like? So there’s a skills and zips spreadsheet. And then there’s like, the service area one which we haven’t included for the skills and zips. Are we

157 00:13:43.390 00:13:50.330 Amber Lin: like, do we have logic say by each page? Is that how is currently set up.

158 00:13:52.250 00:13:57.799 Casie Aviles: I think. Yeah, the the way this was created by Miguel. Then I believe he

159 00:13:58.040 00:14:02.779 Casie Aviles: he reprocessed it himself. It was a little bit more manageable than

160 00:14:02.880 00:14:05.630 Casie Aviles: compared to like the inspector sheet. So he.

161 00:14:05.980 00:14:07.721 Casie Aviles: we did. We didn’t really

162 00:14:08.460 00:14:13.650 Casie Aviles: we we basically worked around the the structure that they already had.

163 00:14:14.550 00:14:19.930 Casie Aviles: Oh, although, yeah, the problem right now is it’s outdated.

164 00:14:20.840 00:14:21.580 Amber Lin: Oh!

165 00:14:21.580 00:14:24.079 Casie Aviles: Using an outdated copy of their

166 00:14:24.080 00:14:28.830 Casie Aviles: wait. I thought I thought they shared a new one with us. Right?

167 00:14:30.820 00:14:33.910 Casie Aviles: Yeah, I do remember they did. They did, although.

168 00:14:34.286 00:14:44.079 Amber Lin: I I thought we transferred it over. If not, it’s okay. We’ll just. We’ll just transfer like, do the transfer. But I just wanna make sure

169 00:14:44.220 00:14:46.640 Amber Lin: wait. Were we on?

170 00:14:47.430 00:14:51.810 Amber Lin: Okay, this is much cleaner than the previous one.

171 00:14:53.860 00:14:59.060 Amber Lin: Yeah, which is why we just work around this, I think, that’s really nice.

172 00:15:02.530 00:15:03.550 Amber Lin: Okay.

173 00:15:08.140 00:15:10.060 Amber Lin: yeah, I think we’ll be.

174 00:15:11.540 00:15:18.660 Casie Aviles: Yeah, they should be much faster to just update. I mean, yeah.

175 00:15:19.290 00:15:23.560 Amber Lin: Yeah, do you?

176 00:15:24.620 00:15:26.789 Amber Lin: Do you think this is?

177 00:15:27.170 00:15:31.480 Amber Lin: We should copy this format for our other spreadsheet?

178 00:15:34.920 00:15:37.130 Casie Aviles: What do you mean like with with the inspector sheet.

179 00:15:37.130 00:15:42.020 Amber Lin: Yeah, do you think it? This is a better format, or the other ones better.

180 00:15:44.080 00:15:47.570 Casie Aviles: I mean, I I still think that the 1st one was

181 00:15:47.890 00:15:49.849 Casie Aviles: I I mean the inspector. One was

182 00:15:51.130 00:15:53.240 Casie Aviles: much better, because it’s more granular.

183 00:15:53.430 00:15:54.090 Amber Lin: Hmm.

184 00:16:04.460 00:16:13.809 Casie Aviles: I I don’t think we’re having any issues with the format itself, like with with the inspector sheet. It’s just, you know, the the AI just gets confused which to use like with the.

185 00:16:13.810 00:16:15.070 Amber Lin: Excuse me.

186 00:16:15.070 00:16:16.490 Casie Aviles: Yeah, which spreadsheet?

187 00:16:16.740 00:16:18.750 Amber Lin: I see, I see. Okay.

188 00:16:19.170 00:16:20.480 Amber Lin: So

189 00:16:20.930 00:16:29.474 Amber Lin: I think the skills and Zips can just stay as it is. For now, I think that’s that’s okay. I don’t think the effort is worth the return.

190 00:16:29.780 00:16:30.359 Casie Aviles: Yes. Okay.

191 00:16:30.360 00:16:36.850 Amber Lin: I think. Well, we could work on adding, Where is it?

192 00:16:37.846 00:16:44.129 Amber Lin: This one, this one. This should be really fast, because this is also very granular.

193 00:16:44.578 00:16:49.820 Amber Lin: It’s by zips. So whatever they ask we can give them answer. I think that would be

194 00:16:50.020 00:16:56.969 Amber Lin: pretty easy. I think I’ll work with you. I’ll give you the what they said. These mean.

195 00:16:57.530 00:16:57.880 Casie Aviles: Okay.

196 00:16:57.880 00:17:02.799 Amber Lin: I think, once we have that we can instruct the AI how to answer those.

197 00:17:03.980 00:17:09.279 Casie Aviles: Okay, I think I will just have to double check like the token size of this, because.

198 00:17:09.280 00:17:10.450 Amber Lin: Oh, okay.

199 00:17:11.099 00:17:17.549 Casie Aviles: Yeah, because if the it’s too much tokens, we might not be able to drop this into the context.

200 00:17:18.839 00:17:19.409 Amber Lin: Because that’s.

201 00:17:19.410 00:17:22.579 Casie Aviles: We’re doing right now right with. Even with the inspector.

202 00:17:22.780 00:17:32.669 Amber Lin: So is that each time they ask about, can we just route it to these spreadsheets, based on what the questions are.

203 00:17:33.485 00:17:35.834 Casie Aviles: Yes, yes, we’re we’re doing that actually.

204 00:17:36.600 00:17:44.860 Casie Aviles: so like, we have another workflow, another AI step that just, you know, takes this in, but I just wanna make sure that it will fit.

205 00:17:45.160 00:17:45.540 Amber Lin: I see.

206 00:17:45.540 00:17:46.470 Casie Aviles: No thanks.

207 00:17:46.470 00:17:50.779 Amber Lin: Yeah, this wouldn’t be bigger than the inspector sheet. It’s the same zip codes.

208 00:17:52.120 00:17:53.640 Casie Aviles: Okay. Okay. Sure. Sure.

209 00:17:53.640 00:18:14.220 Amber Lin: So I think what we’ll we can ask the Csrs when they ask they can say Zip code, or and then add, like inspector or technician or like service area. And based on that, we’ll just standardize the way they ask, because I think that’s easy to do, and that will reduce the amount of tokens, because it’ll just be one spreadsheet. And instead of all 3

210 00:18:15.050 00:18:19.850 Amber Lin: yes, yes, exactly awesome. Let me, I’m gonna move.

211 00:18:20.440 00:18:30.980 Amber Lin: I’m just gonna say, this is, this is done, move back, and then for this cycle.

212 00:18:36.970 00:18:47.890 Amber Lin: And then, oh, to add requirements.

213 00:18:48.010 00:18:50.960 Amber Lin: I think we might need to have a just a quick call

214 00:18:51.290 00:18:54.679 Amber Lin: on that. I think that will make life a lot easier.

215 00:18:57.530 00:19:00.359 Amber Lin: We’ll find a time to do that.

216 00:19:00.590 00:19:01.380 Amber Lin: Okay.

217 00:19:02.730 00:19:10.369 Amber Lin: Coverage tracker format report, scoring initial questions.

218 00:19:11.090 00:19:23.080 Amber Lin: Highlights think we’ll we’ll consider that set up weekly coverage boarding flow coverage tracker. Obvious blocks.

219 00:19:24.350 00:19:30.409 Amber Lin: Yeah. For these to

220 00:19:32.500 00:19:41.870 Amber Lin: How long would it take to? I think we already have it so essentially. What I’m envisioning is that we have all the questions that was asked.

221 00:19:42.000 00:19:47.699 Amber Lin: and then we can use AI to categorize. What type of

222 00:19:48.440 00:19:56.489 Amber Lin: question it is like? Is this more of a dashboard thing? Or is this easier? Say, in Google sheets.

223 00:20:01.273 00:20:05.039 Casie Aviles: Sorry. Can we have like a a bit more context again? Sorry.

224 00:20:05.040 00:20:20.510 Amber Lin: Of course. So I think the goal is that the client wants to understand what types of questions are being asked by people, because right now we only have visibility into the feedback sheet of what? Questions that

225 00:20:20.850 00:20:23.309 Amber Lin: we weren’t able to answer.

226 00:20:24.199 00:20:25.949 Casie Aviles: Yes, I remember. I remember.

227 00:20:25.950 00:20:31.360 Amber Lin: Yeah, is there a way to show just all of the questions?

228 00:20:31.800 00:20:38.320 Amber Lin: I know we kind of have it on the dashboard, but it’s kind of. It’s a bit hard to navigate.

229 00:20:39.070 00:20:45.020 Casie Aviles: Yeah, I think what we mentioned here is we needed to create like a classification.

230 00:20:46.410 00:20:50.209 Casie Aviles: So we want another AI step to just classify, or, or.

231 00:20:50.650 00:20:52.720 Casie Aviles: you know, to to give like labels to each.

232 00:20:52.720 00:20:53.230 Amber Lin: Yeah.

233 00:20:53.920 00:20:54.980 Casie Aviles: Question.

234 00:20:55.690 00:21:02.549 Casie Aviles: So we’ll have to do that with the data that we have on Snowflake, the the conversation logs. So we’ll have to do

235 00:21:02.890 00:21:05.900 Casie Aviles: generate those classifications for all of them.

236 00:21:05.900 00:21:06.929 Amber Lin: I see.

237 00:21:08.170 00:21:12.770 Amber Lin: Oh, question

238 00:21:18.040 00:21:27.910 Amber Lin: and snowflake and show on real dashboard.

239 00:21:28.210 00:21:29.930 Amber Lin: How long would that take.

240 00:21:31.470 00:21:34.019 Casie Aviles: I would estimate around 2 points.

241 00:21:34.850 00:21:35.540 Amber Lin: Okay.

242 00:21:36.965 00:21:37.830 Amber Lin: Next.

243 00:21:40.893 00:21:47.559 Casie Aviles: Also in terms of like having it on real or yeah, we could actually have it on real once we’ve generated the.

244 00:21:47.560 00:21:48.150 Amber Lin: Yeah.

245 00:21:48.150 00:21:52.060 Casie Aviles: The the labels. But yeah, yeah, we could do that.

246 00:21:52.440 00:21:57.330 Amber Lin: Yeah. And then I think Annie can just take care of how it’s gonna show.

247 00:21:57.590 00:22:07.230 Amber Lin: So let’s make this to. I’ll say it is medium priority points.

248 00:22:08.960 00:22:12.360 Amber Lin: Gonna make this.

249 00:22:17.950 00:22:20.740 Amber Lin: add 2 dashboards.

250 00:22:27.900 00:22:28.720 Amber Lin: Okay.

251 00:22:36.270 00:22:36.980 Amber Lin: hmm.

252 00:22:39.510 00:22:41.289 Amber Lin: Going back here.

253 00:22:42.800 00:22:44.310 Amber Lin: Knowledge base.

254 00:22:44.310 00:22:57.859 Casie Aviles: Oh, I just one more clarification. So not not we. We do have all the Inputs from the the Csrs, but not all of them are like our questions. So we to just clarify it’ll only be for questions right?

255 00:22:58.840 00:23:03.600 Amber Lin: Yeah, yeah, I, how would we classified that?

256 00:23:04.440 00:23:10.430 Amber Lin: Because sometimes they don’t add a question mark. They just like, I don’t ask the question mark. Sometimes when I ask AI.

257 00:23:11.250 00:23:18.949 Casie Aviles: Yeah, I guess just yeah. I’ll just have another AI again to do it. And I’ll just verify. I’ll just double check.

258 00:23:20.970 00:23:32.919 Amber Lin: Yeah, it doesn’t need to be too accurate. It just needs to be a rough view, and then I’ll I’ll let this trainers like sort it by date, and then they can just see, okay, in the past week. What are people asking.

259 00:23:33.880 00:23:34.930 Casie Aviles: Yes. Okay.

260 00:23:40.270 00:23:41.590 Amber Lin: Service.

261 00:23:42.240 00:23:43.250 Amber Lin: Alright.

262 00:23:47.570 00:23:54.340 Amber Lin: I’m gonna say, add links to Central Doc.

263 00:23:55.828 00:24:03.739 Amber Lin: Is there a way to show links in Andy’s response, or a URL.

264 00:24:06.744 00:24:11.929 Casie Aviles: Yeah, I think we discussed this previously. We could. We could just hyperlink it.

265 00:24:13.060 00:24:13.410 Amber Lin: Okay.

266 00:24:13.410 00:24:15.120 Casie Aviles: Times, I believe, yeah.

267 00:24:15.930 00:24:18.330 Amber Lin: How long would that take.

268 00:24:20.369 00:24:27.189 Casie Aviles: What? What? What are the links? Again, from like sections from the central dock? We need to link that.

269 00:24:29.995 00:24:34.239 Amber Lin: let me let me show you what they were asking. So

270 00:24:38.080 00:24:39.800 Amber Lin: yeah, here.

271 00:24:42.760 00:24:43.780 Amber Lin: So.

272 00:24:45.720 00:24:47.350 Casie Aviles: And this help her link.

273 00:24:47.500 00:25:01.620 Amber Lin: This is in the Central Doc, and then let me show you what I was talking with one of the Csrs. So this was the response that Andy gave her. And then it says, like, Go to customer, Portal URL.

274 00:25:01.820 00:25:02.700 Amber Lin: And but

275 00:25:02.890 00:25:16.860 Amber Lin: there’s no Urls, yeah, so we can. It doesn’t need to be like a clickable hyperlink, because I think that might be a bit harder I think of as a 1st in as a 1st iteration we can just put the URL there.

276 00:25:17.300 00:25:22.109 Amber Lin: I think. Would that be easier to do, instead of making like making it a clickable link.

277 00:25:23.536 00:25:26.929 Casie Aviles: I think I think it shouldn’t. Shouldn’t be too hard to make it clickable, but.

278 00:25:26.930 00:25:27.570 Amber Lin: Okay.

279 00:25:27.570 00:25:35.679 Casie Aviles: Yeah, yeah, I mean, I mean, it’s just I think it’s going to just be like, in terms of formatting, like what Google chats formatting requires. But.

280 00:25:37.210 00:25:44.459 Casie Aviles: Yeah, I think it shouldn’t be too hard as long as it has the link. I’ll just take a look at the docs again. But yeah, shouldn’t be too hard.

281 00:25:44.960 00:25:53.560 Amber Lin: Okay quick links or shortcuts to Andy’s answer.

282 00:25:54.410 00:25:58.450 Amber Lin: So I’m gonna move this to to do.

283 00:25:58.940 00:26:03.570 Amber Lin: I’m gonna take a screenshot of this thread.

284 00:26:07.670 00:26:08.470 Amber Lin: Oh,

285 00:26:15.780 00:26:19.320 Amber Lin: how many points would this be? Okay? I think we said 3 points.

286 00:26:20.230 00:26:21.080 Casie Aviles: Oh, that!

287 00:26:21.590 00:26:28.470 Casie Aviles: Well, if it’s just no, if it’s just going to be faster, like, if it’s just a matter of formatting

288 00:26:29.133 00:26:33.019 Casie Aviles: it’s going to be 1 point like if if it just.

289 00:26:33.280 00:26:37.139 Casie Aviles: if Andy just needs to add links, then it’s going to be 1 point.

290 00:26:38.410 00:26:41.270 Casie Aviles: I think what we were discussing before was kind of like.

291 00:26:41.270 00:26:42.040 Amber Lin: Clickable.

292 00:26:42.670 00:26:43.930 Casie Aviles: Complicated, but.

293 00:26:44.710 00:26:45.460 Amber Lin: Sorry go ahead.

294 00:26:45.460 00:26:45.960 Casie Aviles: Yes.

295 00:26:46.480 00:26:56.649 Casie Aviles: yeah, yeah. I think the scope is much smaller. Now, if it’s just a matter of adding links where there are where it should work, I mean. Sorry I’m I’m losing my words.

296 00:26:57.830 00:26:59.120 Casie Aviles: Yeah, you know, if it.

297 00:26:59.120 00:26:59.480 Amber Lin: Yeah.

298 00:26:59.480 00:27:02.269 Casie Aviles: The URL is missing. Then we can add that.

299 00:27:03.000 00:27:09.479 Amber Lin: Okay, let’s just do that like, I think it does the same effect. And it takes a lot less of your time.

300 00:27:10.640 00:27:11.320 Casie Aviles: Okay.

301 00:27:14.950 00:27:18.850 Amber Lin: Sorry. Let me go back to this.

302 00:27:19.698 00:27:29.370 Amber Lin: Here’s 1. i think there is one more thing this one.

303 00:27:30.421 00:27:40.870 Amber Lin: So context of this is that we have all these sorry, all these feedback

304 00:27:41.500 00:27:53.339 Amber Lin: right? And a lot of them suggest of like, oh, this is this is what needs to change like this is what we need, Yada Yada Yadda, can we use that to AI generate

305 00:27:53.730 00:28:00.120 Amber Lin: like potential improvements? It’s kind of like the trainer bot. But just to apply it to each of these

306 00:28:00.380 00:28:02.370 Amber Lin: each of these sections.

307 00:28:03.110 00:28:06.119 Amber Lin: If this is for Mustafa like, do you think that’s possible?

308 00:28:06.850 00:28:13.539 Mustafa Raja: Yeah, I just want to know, where do we want? How do we want these suggestions to come up.

309 00:28:16.880 00:28:22.559 Amber Lin: Oh, yeah, yeah, that’s that’s a good point. What is? What are the options?

310 00:28:24.830 00:28:28.460 Mustafa Raja: Who are these suggestions for? Is it.

311 00:28:29.620 00:28:37.660 Amber Lin: These are for the trainers. So the people who’s responsible for creating their like ABC’s training documents.

312 00:28:38.200 00:28:41.719 Mustafa Raja: Okay, this. So for the people who are creating Central Doc.

313 00:28:45.350 00:28:48.970 Mustafa Raja: We can add comments on Central Doc. I feel.

314 00:28:50.840 00:28:53.520 Mustafa Raja: For the areas that need improvement. We can

315 00:28:54.070 00:28:59.300 Mustafa Raja: we can. What’s it called? We can detect them. We detect the areas that need to be improved.

316 00:28:59.660 00:29:02.600 Mustafa Raja: based on the feedback, and then add comments.

317 00:29:02.800 00:29:04.370 Mustafa Raja: But that depends.

318 00:29:04.720 00:29:09.589 Mustafa Raja: But that depends on Google. Docs. Api. If it lets us comment.

319 00:29:10.290 00:29:21.300 Amber Lin: I see. Does it need to be a comment like it can be a lot more raw like. It doesn’t need even need to specify where it is, it can just be. Here’s the improvements.

320 00:29:21.905 00:29:26.160 Amber Lin: Here’s like they would know where it goes into. So.

321 00:29:26.160 00:29:26.870 Mustafa Raja: Okay.

322 00:29:26.870 00:29:31.910 Amber Lin: Like we. I think we just need to give the improvement which we have with the

323 00:29:32.230 00:29:34.096 Amber Lin: we already have with the

324 00:29:35.510 00:29:37.299 Amber Lin: Sorry with a trainer, bot.

325 00:29:38.680 00:29:51.449 Mustafa Raja: Okay. So so let’s say, let’s say, Google, Docs does not let us comment is, it would be, would would it be okay. If if you put this distance at the end of end of the document.

326 00:29:54.820 00:29:57.480 Amber Lin: I think I’d rather have in a different Doc.

327 00:29:57.900 00:30:01.600 Amber Lin: because if we put it at the end of the central dog, it’s it.

328 00:30:01.600 00:30:02.240 Mustafa Raja: It’s going to cost.

329 00:30:02.240 00:30:03.070 Amber Lin: Some more content.

330 00:30:03.070 00:30:04.950 Mustafa Raja: Yeah, well, yeah.

331 00:30:04.950 00:30:06.130 Amber Lin: More Tokens.

332 00:30:06.490 00:30:07.210 Mustafa Raja: Yeah.

333 00:30:08.040 00:30:09.939 Mustafa Raja: Yeah. Another. Doc would be good.

334 00:30:10.956 00:30:19.079 Amber Lin: In a, in a different doc. Okay, suggest changes in a different.

335 00:30:20.590 00:30:21.120 Amber Lin: Oh, my God!

336 00:30:23.370 00:30:26.609 Mustafa Raja: Should we do? Should this be a weekly job.

337 00:30:27.460 00:30:28.509 Amber Lin: Yeah, I agree.

338 00:30:28.970 00:30:31.579 Mustafa Raja: Yeah. So then we should do it in Daxter, right?

339 00:30:36.480 00:30:36.970 Mustafa Raja: Yeah.

340 00:30:36.970 00:30:40.329 Amber Lin: It could be weekly, or it could be like a trigger.

341 00:30:42.520 00:30:43.520 Mustafa Raja: It could be both.

342 00:30:44.110 00:30:48.819 Amber Lin: Okay, weekly cadence or trigger.

343 00:30:49.820 00:30:55.340 Mustafa Raja: Yeah, just let me know if it should be from Dexter. Or do we want to stick to any 10.

344 00:30:57.750 00:30:58.760 Casie Aviles: That’s up to you.

345 00:30:58.760 00:30:59.779 Mustafa Raja: Oh, let’s actually.

346 00:30:59.780 00:31:00.349 Casie Aviles: So let’s stick to.

347 00:31:00.350 00:31:01.520 Mustafa Raja: And it end because.

348 00:31:01.520 00:31:06.569 Amber Lin: It’s it’s easier. I just don’t want you to spend all the time on this client, because this is not like

349 00:31:06.800 00:31:18.039 Amber Lin: this is not a feature that hundreds of people would use. This is literally just me and 2 other trainers. So it can be really, really ugly. I don’t care.

350 00:31:19.485 00:31:19.960 Mustafa Raja: Okay.

351 00:31:19.960 00:31:25.489 Casie Aviles: It. You could do it scheduled on any 11, st Mustafa. Just so it’s faster, and then we could.

352 00:31:25.490 00:31:26.380 Mustafa Raja: Yeah, yeah.

353 00:31:26.700 00:31:27.260 Casie Aviles: Because.

354 00:31:27.260 00:31:35.219 Casie Aviles: don’t just, you know, once it standardized. So that’s why we triggered in Dogster. But yeah, we could start with just anything.

355 00:31:35.810 00:31:46.030 Mustafa Raja: Yeah, yeah, anything would be good in terms of fetching the data, too, because in that stand I have to. Then set up sheets and all.

356 00:31:46.460 00:31:48.710 Mustafa Raja: So anything is a good option to start.

357 00:31:51.310 00:32:01.620 Amber Lin: Yeah. Reviewer, I think Reviewer will manually copy and paste

358 00:32:01.770 00:32:10.440 Amber Lin: edit the document we’re now like, I think I just want the content of the edit.

359 00:32:10.690 00:32:15.250 Amber Lin: Let’s say, let’s say we find a review here.

360 00:32:15.670 00:32:19.139 Amber Lin: Customers to existing service can free, it says.

361 00:32:20.030 00:32:27.520 Amber Lin: needs more detail, creating an order, setting instructor, etc. So I think we’ve

362 00:32:28.540 00:32:31.429 Amber Lin: probably because we already know where.

363 00:32:31.700 00:32:40.789 Amber Lin: where, in the central talk it is, and then based on feedback, provided

364 00:32:41.040 00:32:43.870 Amber Lin: what do you think the workflow would be?

365 00:32:48.640 00:33:15.160 Mustafa Raja: Yeah, the workflow would be, we’ll see the we will get the review. Okay, what the user user is saying about this particular question, and then, we’ll go into the central doc. Identify where where this particular improvement should be done pick that pick that, and then put this into the review document

366 00:33:15.520 00:33:17.929 Mustafa Raja: that this needs to do this.

367 00:33:20.160 00:33:24.070 Mustafa Raja: Yeah, I feel, yeah, this this was the goal, right?

368 00:33:24.270 00:33:33.750 Amber Lin: Yeah, yeah, I think to yeah, just to write like a right better version with AI,

369 00:33:34.010 00:33:37.879 Amber Lin: and then put put new version.

370 00:33:39.520 00:33:43.090 Amber Lin: a section and a different document.

371 00:33:43.090 00:33:44.919 Mustafa Raja: Yeah. So just to clarify.

372 00:33:45.275 00:33:45.630 Mustafa Raja: Oh,

373 00:33:48.006 00:33:57.830 Mustafa Raja: do we want? Do we want the review document to contain? The suggestion of what should be written in Central.

374 00:33:58.080 00:33:59.580 Mustafa Raja: What’s your be replaced.

375 00:34:00.260 00:34:13.840 Amber Lin: Add the original, I think. Just add the original question, feedback section in the Central Doc.

376 00:34:15.820 00:34:28.420 Amber Lin: or just, I think it’s okay. Just just add the new version. I think if they if the trainers read the question and read the feedback they will know what it they will know where it belongs and what it is. I think that’s okay.

377 00:34:29.250 00:34:29.870 Mustafa Raja: Okay.

378 00:34:30.110 00:34:37.820 Amber Lin: New text, actually, not suggestions. But I think just like a written out text of.

379 00:34:37.820 00:34:38.280 Mustafa Raja: Yeah.

380 00:34:38.280 00:34:49.470 Amber Lin: Like what it should be, and then they can edit based on that most of the times. It might not make sense, but like, at least, if some of it makes sense that will be great, how long would this workflow take, then.

381 00:34:51.370 00:34:52.770 Mustafa Raja: Let’s estimate 3.

382 00:34:52.909 00:34:53.489 Amber Lin: Okay.

383 00:34:56.289 00:34:59.569 Amber Lin: Safa, I’ll add this to this cycle.

384 00:35:00.379 00:35:01.539 Amber Lin: All right.

385 00:35:03.379 00:35:12.149 Amber Lin: Yeah, that’s all. 4 that. And then features.

386 00:35:12.749 00:35:16.689 Amber Lin: Okay, quick links, shortcuts.

387 00:35:17.999 00:35:29.129 Amber Lin: I think we don’t need to do that thumbs up Google chat. Compatible.

388 00:35:29.379 00:35:31.049 Amber Lin: Real time trend.

389 00:35:34.659 00:35:37.759 Amber Lin: yeah. Threshold. Smart.

390 00:35:42.366 00:35:43.283 Amber Lin: Okay.

391 00:35:45.019 00:35:51.469 Amber Lin: Did we ever figure out if adding a pending animation is possible in Google Chat.

392 00:35:53.960 00:35:57.729 Casie Aviles: Oh, we didn’t really do the spike for this one.

393 00:35:57.730 00:36:03.999 Amber Lin: It’s okay, not a priority, low priority. I just wanted to check. I remember we did something about that.

394 00:36:04.480 00:36:09.630 Casie Aviles: Wait. I’m not sure if I left a comment, though, can, can. Is it okay? If we check the ticket?

395 00:36:09.890 00:36:13.049 Casie Aviles: Oh, I didn’t. Okay, no problem. Yeah, we didn’t do this fight.

396 00:36:13.050 00:36:14.019 Amber Lin: It’s okay.

397 00:36:15.580 00:36:20.920 Amber Lin: Oh, actually, I think, Mustafa, didn’t you do something for the trainer? Bot of like? Send them

398 00:36:21.290 00:36:24.530 Amber Lin: 1st and then send another message later, when it up in.

399 00:36:24.530 00:36:24.950 Mustafa Raja: And every.

400 00:36:24.950 00:36:25.800 Amber Lin: Bonds.

401 00:36:26.340 00:36:48.050 Mustafa Raja: Yeah, it’s it’s like, what happens in our slack when we when we call a bot, ask it a question. What it says is, there’s a loading animation, and then it says something, and then it edits that message with the reply, right? So this is exactly what’s happening, except the animation. There’s no animation.

402 00:36:49.700 00:36:51.399 Mustafa Raja: I didn’t even look into that.

403 00:36:52.080 00:36:58.979 Amber Lin: Yeah, it’s it’s low priority. Ignore. I don’t think they can survive without that one. It’s like a nice thing to have

404 00:36:59.800 00:37:00.390 Mustafa Raja: Yep.

405 00:37:00.840 00:37:01.460 Amber Lin: I am.

406 00:37:01.460 00:37:05.300 Mustafa Raja: It should be. It should be some brain forge animation.

407 00:37:05.887 00:37:11.729 Amber Lin: I agree it’s complete. Okay, I’ll just add that comment.

408 00:37:15.730 00:37:21.020 Amber Lin: This is back. When we still had emojis in our in our ticket.

409 00:37:21.020 00:37:21.620 Mustafa Raja: Responses.

410 00:37:22.233 00:37:29.640 Amber Lin: In our tickets. Okay, so I think that could be later.

411 00:37:32.230 00:37:39.980 Amber Lin: I think the only thing here is want to add the return. URL return.

412 00:37:43.540 00:37:46.339 Mustafa Raja: Does any have these Urls.

413 00:37:46.470 00:37:50.030 Amber Lin: Good question which I think we need to define.

414 00:37:52.700 00:37:56.870 Amber Lin: So need to.

415 00:37:56.870 00:37:57.440 Mustafa Raja: Become a big.

416 00:37:57.440 00:38:11.739 Amber Lin: Add Urls needed. I think all the Urls. We should just add them into the Central doc. So it will be in the section that Andy should be returning off of. Would that make it easier

417 00:38:11.910 00:38:14.880 Amber Lin: to return, Urls? If it’s in the document.

418 00:38:14.880 00:38:15.890 Mustafa Raja: And Casey would.

419 00:38:16.300 00:38:20.719 Amber Lin: Okay, in Central Doc.

420 00:38:25.710 00:38:26.490 Amber Lin: okay.

421 00:38:26.820 00:38:30.269 Amber Lin: And then I think this was the other one

422 00:38:30.950 00:38:34.320 Amber Lin: that I wanted us to look at for engineering.

423 00:38:34.620 00:38:40.919 Amber Lin: So we have templates like this, Casey. I think we talked about it in grooming.

424 00:38:44.210 00:38:49.229 Amber Lin: I think either you or Mustafa would take this. What do you guys think.

425 00:38:51.540 00:38:52.670 Mustafa Raja: Yeah, I can take it.

426 00:38:53.960 00:39:01.189 Mustafa Raja: So what we what we want is to end what we want is for Andy to return the exact thing right?

427 00:39:01.800 00:39:07.102 Amber Lin: Yes. So when they ask for a template, we should return a template.

428 00:39:07.650 00:39:08.690 Amber Lin: Yes.

429 00:39:10.050 00:39:19.629 Amber Lin: yeah. I just wanna check how much like how much work both of you have. So we can say, who can take that? Once as Annie.

430 00:39:20.240 00:39:28.239 Amber Lin: it’s Casey. Okay, there’s so much stuff that’s that. They have to get back to you.

431 00:39:28.590 00:39:29.120 Casie Aviles: Yeah.

432 00:39:31.000 00:39:34.230 Amber Lin: Okay, so there’s these 2.

433 00:39:34.360 00:39:37.430 Amber Lin: Osava has this one.

434 00:39:37.930 00:39:39.989 Amber Lin: Luke has that? Okay?

435 00:39:40.270 00:39:43.450 Amber Lin: I think you both had like similar amount of work.

436 00:39:43.610 00:39:44.660 Amber Lin: So.

437 00:39:44.660 00:39:45.250 Mustafa Raja: That’s the question.

438 00:39:45.775 00:39:46.300 Amber Lin: Okay?

439 00:39:47.218 00:39:53.329 Amber Lin: So aren’t you also doing like our sales stuff? Or are you both doing a lot of.

440 00:39:53.330 00:39:54.580 Mustafa Raja: Other stuff.

441 00:39:54.840 00:39:56.619 Mustafa Raja: I’m also doing provenance.

442 00:39:57.650 00:40:04.399 Amber Lin: Who of you have more capacity? It doesn’t matter to me. You guys decide if Mustava wants to take it, I’ll assign it to Mustafa.

443 00:40:05.270 00:40:06.075 Mustafa Raja: Yeah.

444 00:40:08.730 00:40:12.619 Casie Aviles: Okay, yeah. I mean up to you up to you myself. If you want.

445 00:40:12.620 00:40:18.099 Mustafa Raja: Yeah, I can. I can take this so we can. We can leave it until until I’m done with.

446 00:40:19.260 00:40:21.379 Casie Aviles: Yeah, I, just yeah.

447 00:40:21.380 00:40:22.500 Mustafa Raja: Yeah, yeah, yeah.

448 00:40:22.500 00:40:22.980 Casie Aviles: That’s.

449 00:40:23.381 00:40:25.388 Mustafa Raja: Let’s also decide a deadline.

450 00:40:25.790 00:40:33.640 Amber Lin: Oh, yes, that’s that’s a good point. I think this is lower priority than the other one, I would say. This one like

451 00:40:33.960 00:40:37.879 Amber Lin: is, I think we can focus on this

452 00:40:38.320 00:40:43.380 Amber Lin: and return. URL return templates, I think would be higher priority.

453 00:40:43.490 00:40:48.170 Amber Lin: I’m just gonna say, both of them for this week.

454 00:40:48.560 00:40:54.490 Amber Lin: and then service area and then fill obvious gaps for like

455 00:40:54.690 00:41:09.479 Amber Lin: next week. Of course, if you complete, if you if you end up having time, you can complete it in this week, and then next week you just don’t have to join any more. Stand ups. That’s also an option, but I think that’s all we all output for like this cycle.

456 00:41:10.100 00:41:10.480 Mustafa Raja: Okay.

457 00:41:11.600 00:41:18.759 Amber Lin: Yeah. So let’s look at that and then we can define how this looks.

458 00:41:18.760 00:41:25.349 Mustafa Raja: Okay? Yeah. So so I have a few questions regarding this. Where are the templates?

459 00:41:25.890 00:41:33.260 Amber Lin: So right now it’s scattered in the central talk. So if you look for template

460 00:41:34.270 00:41:37.279 Amber Lin: like, it’s somewhere in them.

461 00:41:37.540 00:41:45.080 Amber Lin: It’s not like there’s templates for notes, templates for communications, but we can.

462 00:41:45.080 00:41:45.560 Mustafa Raja: Yeah.

463 00:41:45.560 00:41:48.099 Amber Lin: We can modify the central doc if that helps.

464 00:41:49.100 00:41:53.300 Mustafa Raja: Yeah, because I need to somehow identify the templates in the central dock.

465 00:41:54.210 00:41:56.079 Mustafa Raja: I need to pinpoint them

466 00:41:59.355 00:42:02.169 Mustafa Raja: so then I can have them in another table or something.

467 00:42:02.760 00:42:04.719 Mustafa Raja: To pull the exact thing.

468 00:42:05.460 00:42:07.390 Mustafa Raja: And push it to the response.

469 00:42:09.470 00:42:10.460 Amber Lin: I see.

470 00:42:11.790 00:42:14.870 Amber Lin: Collects all templates.

471 00:42:18.020 00:42:25.820 Amber Lin: Add to spreadsheet, and then.

472 00:42:26.170 00:42:27.829 Mustafa Raja: I put them to swapish.

473 00:42:29.126 00:42:30.140 Amber Lin: Super base!

474 00:42:31.600 00:42:32.450 Mustafa Raja: Not fresh.

475 00:42:32.450 00:42:35.700 Mustafa Raja: The what’s it called our embeddings? Are there.

476 00:42:36.030 00:42:37.359 Amber Lin: Aha, I see.

477 00:42:37.750 00:42:45.270 Amber Lin: Okay, so goal be able to.

478 00:42:49.720 00:42:55.729 Amber Lin: And the, I think, returns templates. When asked

479 00:42:55.890 00:43:04.830 Amber Lin: Csrs. Want to be able to copy and paste templates.

480 00:43:05.980 00:43:10.510 Amber Lin: so would it help you if I say.

481 00:43:10.720 00:43:14.070 Amber Lin: see, we have a list of templates, and then

482 00:43:14.460 00:43:21.270 Amber Lin: we put them under the right section. They need to. Or do you just need like a list of templates? It doesn’t matter.

483 00:43:30.002 00:43:37.019 Mustafa Raja: so so so are the templates mentioned in the mentioned in the toc

484 00:43:37.565 00:43:39.540 Mustafa Raja: table of contents or not.

485 00:43:40.756 00:43:44.900 Amber Lin: Some like, not specifically

486 00:43:45.540 00:44:02.090 Amber Lin: the specific template. It’s right now. It’s usually under like a big section of say, it’s about pet poisoning. Or say, it’s about scheduling, then it’s about cancellations. And then under the cancellation section in the text there will be like a cancellation template, but it’s not in the heading.

487 00:44:05.620 00:44:13.150 Mustafa Raja: I’m wondering if if we can wrap it like this. So I’m sending something in chat template.

488 00:44:13.150 00:44:13.670 Amber Lin: Hmm.

489 00:44:20.650 00:44:21.600 Mustafa Raja: Complete.

490 00:44:22.940 00:44:26.479 Mustafa Raja: Would this be okay? I’ve sent something in chat.

491 00:44:27.408 00:44:31.999 Mustafa Raja: So the text would be in center. And then this is wrapped by template.

492 00:44:32.270 00:44:35.021 Amber Lin: Yeah, that’s that’s okay. Yeah. So

493 00:44:35.850 00:44:48.020 Mustafa Raja: Yeah. And that way I’d be able to identify the section it belongs to also, because then it will be in in a section, and we’ll we are already. What’s it called chopping the sections right.

494 00:44:51.210 00:44:52.910 Mustafa Raja: Yeah, this ticket might take some time.

495 00:44:53.430 00:45:01.280 Amber Lin: Yeah. Okay, also send the cancellations template.

496 00:45:03.413 00:45:09.620 Amber Lin: So that’s scenario one and scenario 2 is just to user types.

497 00:45:09.920 00:45:18.280 Amber Lin: It asks, cancellation template.

498 00:45:21.570 00:45:27.039 Amber Lin: and do you returns template? I think this one was.

499 00:45:27.040 00:45:27.479 Mustafa Raja: Take a look.

500 00:45:27.480 00:45:28.749 Amber Lin: Less time, right.

501 00:45:30.180 00:45:38.249 Mustafa Raja: So, for so these scenarios, these are the returns, or of how Andy would return the response.

502 00:45:38.580 00:45:39.360 Amber Lin: Yeah.

503 00:45:40.040 00:45:41.696 Mustafa Raja: The I I meant

504 00:45:42.380 00:45:51.629 Mustafa Raja: what what I meant with this template thing is how? How we are going to structure our templates. And what’s it called Central Doc?

505 00:45:51.630 00:45:57.330 Amber Lin: Yeah, I I totally understand. I think this is something that we need to do regardless. But I was just thinking.

506 00:45:57.330 00:45:57.800 Mustafa Raja: Yeah.

507 00:45:57.800 00:46:10.849 Amber Lin: Because as long as we do one of them, it kind of satisfies the user requirements. So it could be a 1st iteration. Because I think these 2 are different things, and this would take less time than this. One is my is my assumption.

508 00:46:11.710 00:46:15.839 Amber Lin: Like scenario 2 probably takes less less time than scenario. One.

509 00:46:16.660 00:46:19.430 Mustafa Raja: Good scenario to.

510 00:46:22.650 00:46:27.639 Amber Lin: Yeah, they will specifically ask for the template they won’t like. We don’t have to suggest it.

511 00:46:29.320 00:46:31.960 Mustafa Raja: They specifically asked for the template.

512 00:46:35.310 00:46:35.940 Mustafa Raja: Yeah.

513 00:46:37.590 00:46:45.970 Mustafa Raja: yeah, we. This is how how it’s going to be. Scenario 2 is how it’s going to be. I feel

514 00:46:47.129 00:46:50.020 Mustafa Raja: the scenario one will have to implement either we know.

515 00:46:52.010 00:46:52.830 Amber Lin: Is there.

516 00:46:52.830 00:46:53.210 Mustafa Raja: And.

517 00:46:53.210 00:46:55.769 Amber Lin: Said, what would that be?

518 00:46:58.930 00:46:59.390 Amber Lin: Just gonna.

519 00:46:59.390 00:47:05.790 Mustafa Raja: I think we should, we should remove scenario 2. Yeah, this one. We should remove this altogether.

520 00:47:06.390 00:47:06.910 Amber Lin: Because.

521 00:47:06.910 00:47:16.809 Mustafa Raja: Yeah. Remove this because it’s it’s going to be. It’s going to be like the scenario one. No.

522 00:47:18.640 00:47:33.090 Amber Lin: Is it? Cause? I think this one would be say, Csr customer is canceling because of.

523 00:47:33.890 00:47:42.170 Mustafa Raja: Sorry I’m I’m confusing it because the central wrap templates and template template is under this scenario, and

524 00:47:44.170 00:47:44.670 Amber Lin: So.

525 00:47:44.670 00:47:46.219 Mustafa Raja: Okay, yeah, yeah.

526 00:47:46.820 00:47:50.660 Mustafa Raja: Rebecca, yeah. Because.

527 00:47:52.780 00:47:57.680 Amber Lin: Here are steps 1, 2, 3,

528 00:47:58.552 00:48:05.579 Amber Lin: by the way, fill out this template and then template.

529 00:48:07.800 00:48:14.029 Amber Lin: That’s kind of what I envision like. We will have to infer, because they’re talking about cancellations that we’re gonna return this.

530 00:48:14.030 00:48:19.822 Mustafa Raja: Okay, okay, yeah. So so this would be only what’s it called

531 00:48:20.460 00:48:24.070 Mustafa Raja: system, prompt adjustment. So this shouldn’t take much time.

532 00:48:24.310 00:48:25.060 Amber Lin: Oh!

533 00:48:25.680 00:48:26.370 Mustafa Raja: Yeah.

534 00:48:29.151 00:48:33.880 Amber Lin: Okay, so which one is, how long?

535 00:48:33.880 00:48:43.589 Mustafa Raja: Yeah, let’s let’s do scenario one as our goal. And what we have to do is wrap templates in Central Doc in template template.

536 00:48:45.970 00:48:49.190 Amber Lin: I can also just we can just copy all of it over.

537 00:48:49.910 00:48:53.879 Amber Lin: like to a to a spreadsheet that has all the templates.

538 00:48:54.520 00:48:56.629 Mustafa Raja: Yeah, that will be even better.

539 00:48:56.870 00:48:57.550 Amber Lin: Okay.

540 00:48:57.870 00:49:00.680 Mustafa Raja: That even require less time.

541 00:49:00.680 00:49:06.880 Amber Lin: Yeah, I think that will be. That will be the nicest way into a spreadsheet.

542 00:49:07.540 00:49:09.510 Amber Lin: I think that would be the greatest way.

543 00:49:09.780 00:49:12.380 Amber Lin: because wrapping will always miss something.

544 00:49:13.010 00:49:14.460 Mustafa Raja: Yeah, me, too.

545 00:49:14.460 00:49:23.450 Mustafa Raja: And I’d love if the spread. If we have a column in spread spreadsheet that defines which section it belongs to.

546 00:49:25.620 00:49:34.780 Amber Lin: So columns, or be name template.

547 00:49:35.420 00:49:39.940 Amber Lin: And then 6 office. Okay.

548 00:49:41.230 00:49:41.810 Mustafa Raja: Yeah.

549 00:49:42.600 00:49:43.340 Amber Lin: Okay.

550 00:49:45.030 00:49:54.950 Mustafa Raja: Yeah, and then and then if Andy is prompted to give give a template, it’ll go look into this spreadsheet and then pull pull up the most relevant.

551 00:49:54.950 00:49:57.289 Amber Lin: Oh, so we don’t need to add a super base.

552 00:49:57.500 00:49:58.849 Mustafa Raja: Yeah, we don’t need to add to.

553 00:49:58.850 00:50:08.080 Amber Lin: Oh, awesome. Okay. So how long would this ticket take? Because I know 0 2, we’re just prompting this one. We just make a spreadsheet.

554 00:50:08.850 00:50:11.290 Mustafa Raja: Yeah. The 2 points is good.

555 00:50:11.290 00:50:12.830 Amber Lin: Okay. Awesome.

556 00:50:13.260 00:50:15.068 Mustafa Raja: Yeah, this ticket simplified a lot.

557 00:50:15.370 00:50:20.250 Amber Lin: Yeah, I’m I’m glad, cause I like there’s no need to make it so complicated. This is such.

558 00:50:20.250 00:50:23.461 Mustafa Raja: Yeah, small, like, there’s not many people using it.

559 00:50:23.940 00:50:24.620 Amber Lin: Okay.

560 00:50:24.620 00:50:29.510 Mustafa Raja: So so a few questions when will be the spreadsheet ready.

561 00:50:30.777 00:50:32.510 Amber Lin: You. I can.

562 00:50:32.510 00:50:32.930 Mustafa Raja: Okay.

563 00:50:32.930 00:50:35.450 Amber Lin: Try to make it, but you might need to help with that.

564 00:50:35.450 00:50:37.260 Mustafa Raja: I can. I can put all of.

565 00:50:37.260 00:50:39.039 Amber Lin: Them in one place.

566 00:50:39.970 00:50:44.410 Mustafa Raja: Yeah, let me know if I if I can. If I should do this I’ll do it.

567 00:50:45.450 00:50:51.810 Amber Lin: Oh, okay, if you can do that, that will just be great, just like, just feel free to copy and paste all of them in A, in a spreadsheet.

568 00:50:52.100 00:50:57.179 Mustafa Raja: Yeah, I’ll let you know. Once I have the spreadsheet ready, and let me know if I’ve missed any template.

569 00:50:57.180 00:51:04.309 Amber Lin: Okay, yeah. If there’s something missing, the trainers will will tell us it’s like, Oh, this one can’t be. I can’t find it. And then they will.

570 00:51:04.930 00:51:05.640 Amber Lin: Yeah.

571 00:51:05.820 00:51:13.659 Amber Lin: okay, that’s that should be all I’m gonna ask. This is like the the other ones are for me. I think we’re good for this cycle.

572 00:51:14.590 00:51:15.290 Mustafa Raja: Yeah.

573 00:51:15.790 00:51:16.769 Amber Lin: All right.

574 00:51:17.280 00:51:18.650 Amber Lin: Thanks. Everyone.

575 00:51:19.070 00:51:19.440 Mustafa Raja: Thank you.

576 00:51:19.440 00:51:20.239 Amber Lin: Thank you. Guys.

577 00:51:20.560 00:51:21.530 Amber Lin: Bye.