Skip to content

README.md

Latest commit

 

History

History
132 lines (96 loc) · 3.62 KB

README.md

File metadata and controls

132 lines (96 loc) · 3.62 KB

Generative AI Engineering Lab

Generative AI Engineering Lab

Learn Generative AI engineering by building the systems behind real products.

CI License: MIT Node TypeScript GenAI

Quick Start . What You Build . Curriculum . Good First Issues

Why This Repo Exists

Most people do not need another vague AI list. They need a practical path from idea to working system:

  • retrieve trusted context
  • generate grounded answers
  • evaluate outputs
  • scan safety risks
  • plan agent workflows
  • ship with tests and CI

This repo is small enough to understand, but structured like a serious open-source project.

Quick Start

npm install
npm run check

Run demos one by one:

npm run demo:rag
npm run demo:evals
npm run demo:safety
npm run demo:agent
npm run eval:report
npm run build && node dist/src/eval-report-cli.js --json

Run the website:

npm run site

Then open http://localhost:4173.

What You Build

Module What It Teaches Working Code
RAG Retrieval before generation, citations, confidence src/lab.ts
Prompt Evals Expected behavior, forbidden claims, scoring src/lab.ts
Safety Scanner PII, legal, medical, financial, self-harm risk src/lab.ts
Agent Planner Multi-step tool workflows and human handoff src/lab.ts
Production Basics CI, tests, docs, issue templates, roadmap .github/, tests/

Demo Preview

=== Generative AI Engineering Lab ===
rag: cited answer with confidence
evals: prompt quality test passed
safety: high-impact financial risk detected
agent: five-step workflow generated

Repository Structure

src/                      TypeScript implementation
site/                     Static website
tests/                    Smoke tests
curriculum/               Learning path
prompts/                  Reusable prompt templates
evals/                    Prompt evaluation cases
datasets/                 Sample local knowledge base
outputs/                  Reusable artifacts
docs/                     Roadmap, architecture, contribution ideas
.github/                  CI, issue templates, PR template

Curriculum

The course path is intentionally practical:

  1. GenAI foundations
  2. Prompt engineering
  3. RAG systems
  4. Agentic workflows
  5. Evals and safety
  6. Production patterns

Every phase should produce an artifact: a prompt, eval, dataset, agent spec, or code change.

Roadmap

  • Add embedding-based retrieval.
  • Add OpenAI/provider adapter while keeping mock/local mode.
  • Add more eval cases.
  • Add a small browser UI.
  • Add MCP server example.
  • Add lesson quizzes.
  • Add hosted demo.

Current release: v0.1.0

Contributing

Start with docs/good-first-issues.md.

The best first PRs:

  • Add one lesson.
  • Add one eval case.
  • Add one sample dataset.
  • Improve RAG ranking.
  • Add a small CLI command.

License

MIT. Fork it, learn from it, build on it.