# AI Engineering Training Focus for Northcove — Synthesis from 3 Engineer Interviews

**Assumption:** Audience is Northcove's 100-engineer org (Python backend / TypeScript frontend, monolithic app, 4-10 YOE, doubled YoY) `context.md:132-143`. Training goal is applied AI engineering productivity with safety, not generic LLM theory. Noted where evidence is thin.

## Executive Summary

Focus training on 5 convergent needs, in priority order:

1. **Context engineering for Northcove-specific code** — unanimous #1 blocker
2. **Evals, testing & verification for AI output** — both LLM features and AI-generated code
3. **Compliance-safe AI use (regulated data)** — prerequisite for trust, especially with seniors
4. **AI-first + agentic workflows** — close the basic → power-user gap
5. **AI prototyping for planning/alignment** — cheap, already working, reduces meeting load

De-emphasize: deep MCP setup, DSPy optimization, automated DB migrations, AI user-research, PRD generation.

## 1. What all three agree on

### A. "Context is king" but Northcove context is hard to supply
- Ben explicit principle: tag relevant files upfront, don't just say "do the thing" `context.md:110`. Needs org-standard rules/commands/reusable prompts `context.md:113-114`.
- Nina: AI over-engineers complex prompts, cherry-picks wrong legacy patterns; works better with similar concrete examples vs. general prompts `context.md:31-34`. Only basic system prompt today, can't encode detailed standards `context.md:40-42`.
- Raj: AI "not good at Northcove-specific implementations," needs "very prescriptive custom rules" `context.md:171-174`. Knowledge siloed across Confluence/Slack/docs, goes stale `context.md:167,181`.
- Ben adds: production code has antipatterns/coupling/hidden side effects from startup speed; "if you turn one knob, something else falls down" `context.md:83-85`.

**Training implication:** This is module #1. Teach: file-tagging, minimal high-signal context, example-driven prompting, writing/maintaining Cursor rules + commands for Python/TS monolith, how to exclude legacy antipatterns. Build a shared Northcove prompt/rule library during training for buy-in.

### B. Verification gap: plausible-looking output + weak guardrails
- Nina: AI code looks plausible but subtle issues; lacks robust e2e/integration testing and monitoring; risk of accepting "good enough" without verification `context.md:57-61`.
- Nina's production LLM feature (document interpretation, Anthropic with isolation contracts) `context.md:9-14`: manual 100-250 labeled docs, iterative prompt tweaking "more labor intensive than traditional ML" `context.md:16-18`, non-determinism on small changes `context.md:20`, now shifting to proxy metrics at scale `context.md:19`.
- Ben works in correctness-critical space: multi-party calculations, estimates/invoicing/Stripe `context.md:71-73`. He avoids AI for production features for context reasons, but uses it heavily for throwaways/scripts `context.md:83-90`.
- Raj confidence scale: codegen OK "needs human review, especially for database/regulated data" `context.md:165`; "skeptical" on non-trivial features, migrations, regulated-data work `context.md:168`.

**Training implication:** Module #2. Two tracks: (a) evals for LLM features — ground-truth sets, prompt versioning, non-determinism handling, proxy metrics; (b) verification for AI-generated code — targeted tests, review checklists for DB/regulated paths, when to reject. Use Nina's doc-interpretation case as live example. Introduce DSPy/prompt-optimization only as awareness (Ben aware but no time `context.md:107`), not hands-on.

### C. Compliance caution is load-bearing
- Raj: regulated-data exposure concern, prior third-party sharing incident required rework, litigation caution `context.md:175-178`. Legal/compliance always factors in `context.md:153`.
- Nina already uses isolation contracts `context.md:14`; Raj notes review needed for DB/regulated data `context.md:165`.

**Training implication:** Module #3 (short, mandatory, upfront). What data can go to which model/tool, Cursor enterprise boundaries, Stripe/invoicing/regulated-data red lines, incident lesson. Required to win over suspicious experienced devs `context.md:135`.

### D. Org is "AI-second," split between basic and power users
- Ben: never does rough drafts by hand, most org still "AI-second" not AI-first `context.md:77-78`. Everyone on Cursor, value in autocomplete `context.md:79`. Agents for background work (coverage, flag removal, debugging) just starting `context.md:80`.
- Nina self-describes basic vs. Ben power user `context.md:26`; uses chat for scaffolding/baselines/tests/debugging `context.md:22-30`. Wants background agents, better incremental-change prompting, delegating larger features `context.md:52-56,63-66`.
- Raj: tab-complete popular, chat for plan-then-build, code search; "editors rather than authors — rarely >20 lines manually" `context.md:158-162`; agents less adopted `context.md:162`.

**Training implication:** Module #4. Teach AI-first loop explicitly (Ben's list: questions, reproduction, docs, calendar/Jira `context.md:111`), plan-then-build chat workflow `context.md:160`, background agents/subagents hands-on with narrow tasks (coverage, flag cleanup, debug). Address senior skepticism with correctness-critical demos, not hype.

### E. Prototyping works and solves a planning pain
- Ben: current tech specs "too much" — strategy+resourcing+tech in one doc `context.md:94`; proposes 3-4 day onsite + AI prototypes that are "directionally correct, don't need to compile" because "people are visual" `context.md:97-101`. "Directionally correct is the name of the game" for internal scripts `context.md:112`.
- Nina: PM-built Vercel v0 mockups for user testing, lets non-technical iterate without Figma/eng `context.md:44-47`.
- Raj context: thin PRDs, eng does scoping `context.md:136-138`; 1-week PRD-to-deploy for bets `context.md:146`; too many meetings for senior ICs `context.md:180-182`.

**Training implication:** Module #5. AI throwaway prototypes + one-off scripts/internal tools as sanctioned high-ROI use (Ben `context.md:86-90`). Tie to planning reform: slim tech specs, visual alignment. Include non-eng adjacent value (Cursor Slack integration popular `context.md:117`, PM copy/changes `context.md:118`, BI hookup needed `context.md:121`) so engineers know what to support vs. own.

## 2. Divergences to handle in training design

- **Production code:** Ben avoids AI for it; Nina uses for scaffolding/baselines; Raj says OK with review. Don't teach "AI writes your feature E2E." Teach scoped use + human ownership.
- **Tooling depth:** Ben tried MCP for Confluence/Jira/GitHub, hit perf issues, prefers CLI tools `context.md:103-105`; subagents early `context.md:106`. Don't center training on MCP. Teach CLI + DB connection (what works) and note MCP limits honestly.
- **Infra:** Raj flags DB migrations pain + slow pipeline (CI 20-30min, deploy 40m-2h `context.md:150-152`). Don't promise AI fixes migrations; teach skepticism + guardrails there.

## 3. Recommended curriculum (prioritized)

**Must-have (Day 1):**
1. Context Engineering for monolith (90 min, hands-on in own repo): tagging, example-first prompts, Cursor rules/commands, anti-pattern exclusion. Output: contributed team rule.
2. Verification & Evals (90 min): review checklist for AI diffs; eval harness demo using doc-interpretation pattern (100-250 labels → proxy metrics); e2e/monitoring minimums.
3. Safe & Compliant Use (30 min): data boundaries, isolation contracts, regulated-data decision tree + incident recap.

**High-value (Day 2 / follow-up):**
4. Agentic Workflows (60 min): background agents for coverage/flag-removal/debug; prompting for incremental changes; when to delegate vs. pair.
5. Prototype-to-Align (60 min): directionally-correct sketches, v0 for PMs, slim specs; internal scripts clinic.
6. Knowledge workaround (30 min): pragmatic retrieval from stale docs/Slack without heavy MCP; team norm for keeping rules/docs fresh + Slack channels (`cursor-nerds`, `background-agents` `context.md:81`).

**Defer / light touch only:** MCP server building, DSPy full implementation, AI PRD generation (blocked by product workflows `context.md:166`), AI user research (n=3 volume too low `context.md:169`), DB-migration automation.

## 4. Format notes

- Hands-on in Northcove monolith, single-engineer scope (matches "most projects are single engineer" `context.md:148`); avoid toy repos.
- Level-split exercises: Nina-basic track (scaffold/test/debug) + Ben-power track (agents/subagents, prototype seeding).
- Leave with artifacts: shared rules/commands library, eval template, compliance one-pager — addresses "need foundation so everyone can benefit" `context.md:120`.
- Measure: % PRs with AI-assist + human review note, eval coverage on LLM features, reuse of shared prompts, reduction in spec churn/meeting load — aligns to Ben's platformy metric focus (loss/accuracy/recoup timeliness `context.md:74`) and Raj's OKR structure `context.md:139`.

## 5. Assumptions noted
- No direct access to codebase, Cursor config, or compliance policy; assumes Cursor enterprise default + Anthropic isolation as stated.
- Assumes training time is limited (1-2 days); if only half-day available, do modules 1-3 only.
- Assumes Platform/Business/Consumer silos `context.md:128-131` share stack enough for common rules; may need per-silo customization.