Debugging a 7-Agent Prompt Framework with Itself
The author built a 7-agent prompt framework (C.E.H.) to run on local LLMs and used it to build a RAG system. When the system encountered issues, the author used C.E.H. to debug its own output, leading to a recursive loop that was resolved by making the prompts more prescriptive.
Why it matters
This approach demonstrates how a well-designed prompt framework can be used to debug and improve its own output, highlighting the potential of AI systems to be self-correcting.
Key Points
- 1The author built a 7-agent prompt framework (C.E.H.) to build a RAG system without external APIs
- 2When the system encountered issues, the author used C.E.H. to debug its own output
- 3The initial prompts led to a loop, which was resolved by making the prompts more prescriptive
- 4The evidence gate in C.E.H. prevented agents from claiming completion without test results or diffs
Details
The author built a 7-agent prompt framework called C.E.H. to run on local LLMs and build a RAG system without any external API calls. The framework includes agents for project management, coding, testing, and debugging. When the system encountered issues, the author used C.E.H. to debug its own output, leading to a recursive loop. This was resolved by making the prompts more prescriptive, with explicit find-and-replace instructions instead of open-ended tasks. The evidence gate in C.E.H., which requires agents to provide test results or diffs before claiming completion, helped prevent agents from lying about their progress.
No comments yet
Be the first to comment