Mike Woodward has written a practical guide to Ralph, a methodology that automates code generation by working around LLM context limits. The core idea is simple: instead of asking an LLM to build an entire project at once, you slice it into "bite-size requirements" with specific acceptance criteria. The system tests each chunk, retries if it fails, and moves on when it passes. No human sits in the loop during execution. You write the requirements, start the process, and come back to a finished app.
The name comes from Ralph Wiggum, the slow but well-meaning Simpsons character. Woodward explains that if you were instructing Ralph to build something, you'd break it into pieces and test each one before moving on. It might be slow, but you'd get it done right. That's the philosophy here. The LLM works through a Product Requirements Document (PRD) section by section, with each section containing a description and acceptance criteria. When code passes, the system marks that requirement complete and moves to the next.
Getting started requires a code generation CLI (Claude CLI or Cursor CLI both work), a skills-based architecture for your LLM, and a Git repository. You write your PRD in Markdown, convert it to JSON using a bash script, then run the loop. Your job shifts from coding to product management. This shift mirrors the trend of product engineers emerging from engineers and PMs alike. Woodward notes that the existing Ralph documentation was either too theoretical or not practical enough, which is why he wrote this guide. Hacker News comments back this up. Users report the initial setup is genuinely tricky.
You give up fine-grained control over the code in exchange for automation. The process only works if your requirements are well-written and your acceptance criteria actually catch failures. Woodward mentions that once prompts exceed a page or two, code quality drops and the LLM starts missing things. Ralph sidesteps this by keeping each chunk small. It's not free either. You need an active subscription to whatever AI you choose. But if you can write good requirements, you can build small apps over lunch and bigger ones while you sleep.