I will fix and optimize rag chatbot retrieval and hallucinations

Does your RAG chatbot retrieve weak evidence, cite the wrong source or underperform as your data grows?

I diagnose, fix and optimize existing RAG chatbots that miss relevant passages, rank evidence poorly or answer beyond their sources.

I inspect ingestion, chunking, metadata, lexical and vector search, hybrid fusion, ranking, context construction, citation mapping and insufficient evidence behavior. You receive reproducible test cases, prioritized findings and clear before and after results. Standard and Premium include agreed code changes.

My production retrieval backend combines full text and semantic search with weighted rank fusion, source deduplication, evidence reading, caching and regression checks. When appropriate, I can optimize basic vector retrieval into a better tested hybrid pipeline. I focus on measurable retrieval accuracy, not prompt changes alone.

I work with Python or JavaScript, REST APIs, FastAPI, Cloudflare Workers, PostgreSQL pgvector, Qdrant, SQLite FTS5 and comparable stacks.

Message me before ordering with your stack, failure examples, access method and 2 to 5 real questions.