AI Coding AssistantL05 · Module I · Lesson 01 of 1
Lab · 90 min

Lab: Build an AI coding assistant

For a defined codebase, tested and shipped.

Summary

A domain-specific coding assistant with real evaluation. Not a wrapper around Copilot — an assistant tuned for one codebase, with tests that measure whether it helps.

Objectives
  • 01Build an assistant for one defined codebase.
  • 02Provide it with domain context via retrieval or fine-tune.
  • 03Instrument suggestions and acceptance rates.
  • 04Evaluate correctness with tests, not vibes.
Key Ideas
  • A coding assistant lives or dies on retrieval quality over the codebase.
  • The only honest metric is 'did the tests pass after the suggestion?'
Lab

Build a coding assistant specialized for one codebase you know (an open-source library or a defined internal repo). Provide it with codebase context via retrieval over source, docs, and issue history. Ship it as a CLI or IDE extension. Instrument suggestion generation, acceptance, and rejection. Evaluate on 30 real tasks pulled from the repository's issue history: does the suggestion make tests pass? Report acceptance rate and pass rate.

Deliverables

  • Working assistant (CLI or IDE extension).
  • Retrieval index over source, docs, and history.
  • Instrumentation logs with suggestion/acceptance/pass data.
  • Evaluation report on 30 real tasks.

Rubric

  • Specialization — measurably better than a generic assistant on this codebase.
  • Instrumentation — you can answer 'is it working?' with data.
  • Correctness — evaluated with actual tests, not judgment calls.
  • Deployability — installable by another engineer without your help.