RAG Pipeline Development —
Production, Not Prototype

The right vector database depends on your scale and infrastructure. Pinecone: fully managed, easiest to start, best for teams without infrastructure expertise — excellent production choice up to 100M vectors. Weaviate: open source, strong native hybrid search (BM25 + vector), good for self-hosted setups. pgvector: if you already use PostgreSQL, adding pgvector keeps your stack simple and queryable via SQL. Qdrant: high performance with strong filtering and on-disk indexing for large datasets. A Starter session (4hr / $100) with a QuickHire RAG engineer is ideal for making this decision before committing to an architecture.

QuickHire RAG engineers handle: PDFs (including scanned PDFs with OCR), Word documents, HTML/web pages, Markdown, code repositories, JSON/CSV structured data, Confluence/Notion pages, email archives, transcripts, and proprietary document formats via custom parsers. For each document type, the engineer selects an appropriate chunking strategy — what works for legal PDFs (semantic chunking by section) differs from what works for code files (function-level chunking) or conversational transcripts (turn-based chunking).

QuickHire RAG engineers implement evaluation using RAGAS metrics: Context Recall (are relevant chunks retrieved?), Context Precision (are retrieved chunks actually relevant?), Answer Relevance (does the generated answer address the query?), and Faithfulness (is the answer grounded in retrieved context, not hallucinated?). Beyond RAGAS, we implement LLM-as-judge evaluation for domain-specific quality, a golden dataset of question-answer pairs with known correct sources, and regression testing to ensure RAG improvements do not degrade other query types. An evaluation pipeline is a deliverable, not an afterthought.

Cost optimization in production RAG involves: embedding model selection (smaller models like text-embedding-3-small vs large are 5x cheaper with minimal quality loss for most use cases), caching frequently retrieved chunks to avoid redundant vector searches, query classification to route simple queries to keyword search and complex queries to semantic search, chunking strategy tuning to reduce chunk count (fewer, more precise chunks = fewer tokens passed to the LLM), and asynchronous batch embedding for document ingestion rather than real-time embedding per document. A QuickHire RAG engineer can audit an existing RAG system for cost optimization in a Starter session.