Project RAGGY: Why It Failed

In the world of AI, failure is often more instructive than success. Project RAGGY (Retrieval-Augmented Generation for General Yield) was our attempt to build a "universal knowledge base" that could instantly answer any question about our internal codebases.

The Ambition

We wanted to solve the context window problem. Instead of feeding an LLM entire files, RAGGY would index every function, class, and comment in a vector database. The goal was to have an AI that "knew" the entire project history without needing to read it all at once.

The Roadblocks

The theory was sound, but the reality was messy. We encountered three fatal flaws:

1. Stale Data: Code changes fast. Keeping the vector database in sync with the live repo proved to be a computational nightmare. RAGGY was often confident but wrong, citing code that had been deleted days ago.
2. Context Fragmentation: By chopping code into small chunks for retrieval, RAGGY lost the "big picture." It could explain a single function perfectly but failed to understand how that function fit into the broader architecture.
3. Cost vs. Value: The cost of embedding and re-embedding code for every commit outweighed the time saved by using the tool.

The Pivot

We shut down RAGGY in late October 2025. But the technology wasn't wasted. We realized that instead of a "universal know-it-all," we needed a "current-context expert." This led to the development of the Context-Aware Monitor in the AI Manager, which focuses only on the active task and relevant files, rather than trying to memorize the entire history.

RAGGY failed so the AI Manager could succeed.