System StatusOnline
NAV
Production RAG pipeline with vector database, hybrid search, and reranking. Search millions of documents in <100ms with semantic accuracy.
I build production-grade Retrieval-Augmented Generation systems: user question → hybrid vector + keyword search → cross-encoder reranking → context injected into LLM → accurate answer with citations. I implement and tune vector databases (Pinecone, Weaviate, Milvus, pgvector), optimize chunk strategies for your document types, and build reranking pipelines that push retrieval precision above 90%.
Hybrid search combining dense vector similarity with BM25 keyword matching—captures both semantic intent and exact terminology for highest recall.
Cross-encoder reranking pipeline that re-scores retrieved documents for true relevance before passing context to the LLM—dramatically fewer irrelevant answers.
Smart document chunking tailored to your content type: by heading hierarchy for docs, by clause for contracts, by semantic boundary for general text.
Metadata filtering and namespace isolation—search within a specific project, date range, or document type without degrading latency.
Hallucination prevention layer: source attribution, confidence scoring, and a 'no answer found' fallback when retrieved context doesn't support the question.
I build the ingestion pipeline: parse documents, apply optimal chunking, generate embeddings with the best model for your language and domain, and index with metadata.
I configure the vector store with HNSW index parameters optimized for your dataset size, set up metadata schemas for filtering, and benchmark retrieval latency.
I build hybrid search (dense + BM25), add a cross-encoder reranker, tune similarity thresholds, and implement metadata-based pre-filtering for sub-100ms end-to-end latency.
I wire the retrieval pipeline to the LLM with precise prompt engineering, implement citation tracking, and evaluate end-to-end accuracy on 200+ test questions before launch.
Pinecone is the easiest to start with (fully managed, no ops). Weaviate offers richer features like built-in hybrid search and BM25. pgvector is the most cost-effective for small to medium scale if you already use PostgreSQL. I recommend based on your scale and infrastructure constraints.
For factual retrieval from specific documents, RAG is almost always better—it's faster to implement, cheaper to update, and doesn't hallucinate facts outside its documents. Fine-tuning excels when you need to change the model's reasoning style or output format, not its factual knowledge.
I use recall@k and NDCG metrics on a labeled test set of question-document pairs. I tune chunk size, embedding model, and reranker threshold until retrieval meets targets before integrating the LLM layer.
Initiate protocol. Establish connection. Let's build something loud.