Learning & Development (L&D)
Purpose: Single home in the vault for Brainforge internal learning and development: curricula, certification tracks, plans, and pointers to related context.
Lead: Brylle Girang
Program: Brainforge AI L&D Program Q2 2026 — Status: Active — Quickstart and AI Accelerator curricula live in-vault April 2026
Related roadmap: plans/q2-2026-ld-roadmap.md · Notion — Brainforge AI L&D Program Q2 2026
Scope (internal first): AI adoption, structured upskilling, assessment, and—over time—artifacts that could support an external L&D / training offering. Client-facing deliverables stay in client repos; internal transcripts and strategy notes follow vault rules below.
Repository map
| Path | Use |
|---|---|
NORTH-STARS.md | L&D frameworks, mindsets, and design standards — governs all modules, certifications, and curricula |
course-authoring-playbook.md | How to build courses, modules, and lessons (templates, folder layout); pair with ld-course-builder skill |
guide-authoring-playbook.md | How to build Enablement Guides for one workflow and one behavior change |
platform-page-standard.md | How courses present on The Forge (overview, module redirect + intro lesson, lesson layout), typed platform config, QA; use with ld-course-builder Mode D |
guide-page-standard.md | How Enablement Guides present on The Forge at /learning/guides |
programs/ | Course modules, learning paths, rubrics — Quickstart: programs/courses/foundation/ · AI Accelerator: programs/courses/accelerator/ · Index: programs/courses/ |
programs/guides/ | Enablement Guides for moment-of-need workflow learning in live work |
programs/doordash/ | Doordash workflow — weekly PR scan, packaging guide, SLA, and delivery changelog (Initiative 3) |
plans/ | Quarter or initiative plans owned by L&D (timeline, objectives, milestones), including the Q2 2026 L&D roadmap, the Doordash workflow plan, and the Refire workflow (Linear-only) |
resources/ | Links, vendor pages, budgets, templates—lightweight reference only |
engineering/ | Supabase / Forge data notes for L&D (e.g. progress schema) |
Program purpose
The Brainforge AI L&D Program systematically builds the team’s capacity to use AI tools in daily work — not as a side experiment, but as the default way we operate. The program is structured around tracks: the Quickstart track is required for all team members; additional tracks will be added as the team’s capability matures.
The program is not about awareness or training completions. It is about measurable behavior change: team members who consistently use AI-integrated workflows, produce better client outcomes faster, and contribute to the team’s shared knowledge base.
Enablement Guides complement the track model. They are not mini-courses. They are lightweight, reusable workflow guides for moments when someone needs to perform one Brainforge task correctly now.
Track index
| Track | Status | Audience | Time | Entry |
|---|---|---|---|---|
| Quickstart | Active (April 2026) | All team members | ~2.5 hours | No prerequisite |
| AI Accelerator | Active (April 2026; Applied Task disabled) | All team members (after Quickstart) | ~3 hours | Quickstart complete |
| Vendor Certifications | Active (Q2–Q3 2026) | Data, AI, Strategy service lines | Varies by cert | Role-dependent |
| Enablement Guides | Active (April 2026) | Brainforge team members at moment of need | 5–15 min each | None |
| Delivery-Sourced Opportunities | Draft (April 2026) | Delivery leads, service leads, senior ICs; CSOs optional | ~3 hours | Quickstart complete (recommended) |
| Advanced Delivery | Planned (Q3 2026) | CSOs and EPs | TBD | Quickstart complete |
| Data & Analytics | Planned (Q3 2026) | Data engineers | TBD | Quickstart complete |
| GTM & Growth | Planned (Q3 2026) | GTM team | TBD | Quickstart complete |
Quickstart + AI Accelerator
Milestone: M2.1 — April 6, 2026
Quickstart path: knowledge/people/learning-development/programs/courses/foundation/
AI Accelerator path: knowledge/people/learning-development/programs/courses/accelerator/
Quickstart (required baseline) — see Quickstart README:
- Cursor + GitHub Setup
- Delivery Standards × AI
- Cursor Deep Dive — prompting, skills, modes, MCP vs CLI, model selection, troubleshooting
- GitHub for Brainforge
- The Forge
AI Accelerator (after Quickstart) — deeper orchestration and advanced patterns; see AI Accelerator README for the active five-module sequence (Linear MCP → Automations → Cloud Agents → Building Skills → Advanced Agents). The older Applied Task module remains in the vault as a disabled reference copy.
The Forge hosts the active AI Accelerator sequence. The archived Applied Task module remains vault-only.
Program design principles
Expanded reference: Full frameworks, maturity model, exclusions, and evidence stack are documented in NORTH-STARS.md. The principles below are the short form; use NORTH-STARS for authoring, rubric design, and QA.
1. Backward from behavior, not from content.
Every module is designed from observable behavior change first — what a team member should be able to do differently after completing the module. Content is in service of behavior, not the reverse.
2. Criterion-referenced, not norm-referenced.
Certification is based on a fixed standard (the rubric), not on comparison to other team members. Every team member can pass. The rubric describes what “meeting the standard” looks like; it does not grade on a curve.
3. Real work, not simulations.
Every applied task uses real clients, real vault data, and real Linear boards. The Quickstart certification task produces a real artifact (a board audit saved to the vault and a real Linear ticket). Learning in context transfers better than learning in simulations.
4. Spaced practice over one-time training.
Certification is the start of the habit, not the end. Each track embeds a post-certification reinforcement schedule — spaced practice anchors in Week 1, Week 2, and Week 4. Learning that doesn’t change how you work on Tuesday was not retained.
5. Team knowledge compounds.
Skills built in Accelerator A1, feedback sessions written in the GTM feedback loop, and vault entries created in daily work all contribute to the team’s shared knowledge base. The program is designed so that individual learning becomes team infrastructure.
Doordash workflow (Initiative 3)
The Doordash workflow is how the L&D team disseminates platform and service-line updates to the team. It automates the intake stage by scanning merged GitHub PRs weekly, classifying what changed into three tiers, and packaging each update in a format suited to its complexity — weekly digest, standalone Slack post, or Forge page with Zoom Clip.
Key files:
programs/doordash/— operating docs (PR scan guide, packaging guide, SLA, changelog)plans/doordash-workflow.md— full workflow plan (five stages, education science rationale, implementation order)
Status: Active — launched April 17, 2026 (M3.1)
Paired with: Refire workflow (M3.2) — Linear as system of record for skill/workflow feedback
Related content elsewhere in knowledge/
- People / HR (adjacent):
../recruitment/,../hiring/,../roles-levelling/ - Company-wide people plans:
../../plans/people/(e.g. quarterly people plan placeholders) - Internal meeting transcripts (L&D strategy): often under
../../clients/unassigned/transcripts/(search filenames forld,L&D,learning). Prefer over time moving internal-only Brainforge syncs to../../meeting/per vault conventions.
Open PRs (confirm when merging)
| PR | Summary |
|---|---|
| #560 | Adds the Quickstart curriculum plan at knowledge/people/plans/planned/foundation-curriculum-build-2026.md. After merge: consider moving that file into learning-development/plans/ so all L&D plans live under this home (optional cleanup PR). |
Conventions
- Date-stamp planning and program docs where it helps (
YYYY-MM-DDin filenames or front matter). - Owner on substantive docs: Brylle or delegate, with status (draft / active / archived).
- No secrets in this tree—use env / 1Password per repo rules.
Questions
Slack: #ai-learning | Owner: Brylle Girang
Last updated: 2026-04-17