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Adding a RAG pipeline to GenePattern Copilot actually degraded performance, making the chatbot's answers worse than the baseline frontier model. #RAG

A decade of messy help forum data had effectively turned the vector database into a high-volume noise generator that corrupted the model's reasoning. Reversing this required abandoning traditional token-count chunking for an LLM-driven atomic fact extraction strategy. #DataEngineering

The platform answers complex bioinformatics and GenePattern system questions. The initial architecture used LangChain, LangGraph and ChromaDB to query a decade of help forum answers, notebooks, platform docs and genomics tool specs. #Bioinformatics

However, evals showed that RAG degraded output quality compared to the standalone model. Observability exposed a severe signal-to-noise problem: the retrieval layer was actively corrupting the model's reasoning with irrelevant or fragmented context. #LLM

The root cause was a naive, size-based chunking strategy. Fixed token counts frequently bisect paragraphs, break context windows and separate solutions from their original questions -- especially destructive for forum threads. #MachineLearning

Isolating facts prevents text within chunks from referencing external, missing context. Resolving these dependencies at the ingestion layer ensures vector matching captures precise semantic intent, drastically improving retrieval quality. #NLP

Ultimately, RAG performance is a data engineering problem rather than a retrieval framework problem. Clean data at ingestion outweighs complex orchestration later in the pipeline. #GenAI