I will audit and optimize your rag pipeline for better retrieval

Your RAG pipeline retrieves wrong chunks and your LLM hallucinates. The problem is not the model. It is the retrieval.

I optimize RAG systems with 9+ years in search relevance engineering. Most RAG failures are retrieval failures, and I fix them at the source.

What I fix:

- Poor chunking strategies that split context

- Wrong embedding model selection

- Missing reranking stage

- No hybrid search (keyword + vector combined)

- No retrieval evaluation metrics

What you get:

- Retrieval quality assessment with before/after metrics

- Chunking strategy optimization for your data

- Embedding model evaluation and recommendation

- Reranking pipeline setup (cross-encoder or ColBERT)

- Hybrid search implementation (BM25 + vector + RRF)

- Evaluation framework (precision, recall, NDCG, MRR)

I work with any stack: LangChain, LlamaIndex, custom pipelines. Vector stores: Elasticsearch, OpenSearch, Pinecone, Weaviate, Qdrant, Meilisearch, pgvector.

This is not a chatbot builder gig. I fix retrieval quality so your LLM stops hallucinating.

Message me with your current stack, data type, and the problems you are seeing before ordering.