Phase 1: Test-Driven AI Development · 90 min · Python · pytest · subprocess
Project 1: TDD Verification Pipeline for AI-Generated Code
The pipeline is the product. Specs in, verified code out.
Hiring signal: Engineers who can build a TDD verification pipeline that automates the spec-to-verified-code workflow demonstrate the ability to scale AI code quality practices across a team. This is the kind of tooling that AI-first engineering teams need and that hiring managers at companies using AI at scale actively look for.
What you will learn
- Build a complete TDD verification pipeline: spec -> tests -> AI code generation -> verification -> iteration
- Integrate AI coding assistants into an automated pipeline using subprocess or API calls
- Handle iteration limits, failure cases, and timeout scenarios
- Produce a working pipeline with 3 demo specifications and iteration logs
The Problem
You have learned the AI TDD loop manually: write a spec, generate tests, prompt the AI, run tests, iterate. But doing this manually for every function is tedious. In a real team using AI assistants at scale (75+ PRs/day), you need automation.
This project builds a TDD verification pipeline that automates the loop:
Specification (JSON)
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Test Generation (from spec)
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AI Code Generation (prompt AI with spec + tests)
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Test Execution (run pytest)
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Failure Analysis (parse test results)
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Feedback Loop (feed failures back to AI)
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Repeat until all tests pass or max iterations reached
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Verified Code + Iteration Log
Pipeline Architecture
The pipeline has 5 components:
1. Spec Loader
Reads a JSON specification file containing the behavioral spec (from Lesson 2).
2. Test Generator
Generates pytest test cases from the spec. Uses the SpecToTestTranslator from Lesson 2.
3. AI Code Generator
Prompts the AI assistant with the spec and tests. In this project, we simulate the AI with a simple template-based generator. In production, you would call the Claude API, GitHub Copilot API, or use subprocess to invoke Claude Code / Cursor.
4. Test Runner
Runs pytest against the generated code and parses the results (pass/fail counts, failure messages).
5. Iteration Controller
Manages the loop: feeds failures back to the AI, tracks iterations, detects regressions, and enforces the 5-iteration limit.
The pipeline is a framework, not a one-off script
The pipeline is designed to be reusable: drop in any specification JSON, and it produces verified code. This is the same pattern you will use in Phase 7 (CI/CD pipelines for AI code) -- the pipeline becomes a CI step that automatically verifies AI-generated code before it reaches review.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers Demo Specifications, Running the Pipeline, Build It — plus a hands-on lab, quiz, and project artifact.
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