Ingestion pipelines that process your documents at scale: PDFs (including scanned PDFs via OCR), Word files, HTML pages, markdown, spreadsheets, database exports, and structured JSON/CSV data. Chunking strategy is one of the highest-leverage decisions in a RAG pipeline -- the wrong chunking approach degrades retrieval quality regardless of the model quality. We implement fixed-size chunking with overlap for uniform documents, semantic chunking (using embedding similarity to find natural topic boundaries) for long-form content, hierarchical chunking (parent-child relationships) for documents where summary context matters, and document-structure-aware chunking that respects section headings, tables, and lists. Metadata extraction at ingest -- document type, author, date, department, access control tags -- enables filtered retrieval that combines semantic search with hard constraints. Incremental update pipelines re-index only changed or new documents, not the full corpus, keeping your knowledge base current without the cost of full re-processing.

Vector database selection, setup, and configuration based on your scale, infrastructure preferences, and query latency requirements. Pinecone for fully managed, serverless vector search at any scale without infrastructure overhead. Weaviate for multi-modal retrieval (text and images) or when you need built-in BM25 alongside vector search. pgvector for teams already on PostgreSQL who want vector search without adding a new infrastructure dependency. Qdrant for on-premises deployments with strict data residency requirements. Embedding model selection based on benchmark performance on your specific domain: OpenAI text-embedding-3-large for highest quality on English text, Cohere Embed for multilingual requirements, or open-source models (BGE, E5, GTE) when data privacy or cost constraints preclude sending content to a third-party API. Multi-tenant index architecture with namespace or tenant ID filtering for platforms serving multiple customers from shared infrastructure.

Hybrid retrieval combining dense vector search with BM25 sparse keyword search, consistently outperforming either approach in isolation -- vector search retrieves semantically similar content that doesn't contain the exact query terms; BM25 catches exact-match keywords that vector search may miss (product codes, names, acronyms). Combined via Reciprocal Rank Fusion or a learned weighting function. Re-ranking using a cross-encoder model (Cohere Rerank, BGE reranker) or LLM judge scores each retrieved chunk against the original query for actual relevance, not just similarity -- the top-20 vector search results are re-ranked and the top 3-5 are selected for context. Query expansion generates multiple reformulations of an ambiguous query and retrieves for each, increasing recall for short or underspecified questions. HyDE (Hypothetical Document Embeddings) generates a hypothetical answer to the query and uses its embedding for retrieval when the query structure doesn't match the document structure.

RAG evaluation built before the system goes to production, not added after users start complaining about answer quality. Evaluation dataset built from your actual query distribution (not synthetic examples that don't match real user behavior), paired with expected answers and the source documents that should ground them. Automated scoring across four dimensions: context recall (did retrieval find the relevant chunks?), context precision (are the retrieved chunks actually relevant to the query?), faithfulness (does the generated answer reflect what's in the retrieved context?), and answer relevancy (does the answer address the user's actual question?). RAGAS framework or custom evaluation pipeline depending on your tooling preferences. LLM-as-judge scoring for qualitative dimensions that don't have a ground-truth answer. Regression test suites run in CI/CD so that prompt changes, chunking modifications, and embedding model updates are validated against the quality baseline before deployment.

The RAGAS framework is our default evaluation harness: it exposes the four core metrics as a structured pipeline that runs against your evaluation dataset on every significant change. For teams that need full traceability, LangSmith tracing captures every retrieval call, every re-ranking step, every prompt sent to the LLM, and the final response -- with latency breakdowns at each stage -- so you can pinpoint exactly where quality degrades on failing queries. Embedding model benchmarking is run at project start on a sample of your actual documents: text-embedding-3-large (OpenAI) vs. BGE-M3 (BAAI, open-source) vs. Cohere Embed v3 on your specific domain corpus, because the model that performs best on MTEB benchmarks does not always perform best on your domain vocabulary. Chunking strategy evaluation tests fixed-size (512 tokens with 64-token overlap) vs. semantic chunking (embedding-similarity boundary detection) vs. hierarchical (parent document with child chunks) on your document types, with retrieval recall measured at each configuration before a strategy is selected. Query transformation evaluation tests whether HyDE (generating a hypothetical answer and using its embedding for retrieval) or step-back prompting (broadening the query to the underlying concept) improves recall for your specific query failure modes. All evaluation results are stored and versioned so quality trends over time are visible, not just the current snapshot.

Production Q&A systems built on RAG for the use cases where staff or customers need accurate answers from your specific documents rather than general knowledge: internal policy and procedure lookup, product documentation search, compliance and regulatory reference, customer support knowledge bases, and sales enablement research. Answer generation with source citations displayed alongside the answer -- not as footnote numbers but as linked document excerpts that users can verify in one click, addressing the user trust problem that kills adoption when answers can't be traced to their source. Multi-turn conversation handling maintains context across follow-up questions within a session. Confidence scoring and fallback responses for queries outside your knowledge base scope ("I don't have information about this in our documentation") rather than confidently wrong answers. User feedback (thumbs up/down, answer corrections) flows into the evaluation dataset and the continuous improvement cycle.

Enterprise RAG for organizations with large, heterogeneous, and access-controlled document collections: legal databases where only certain departments may access certain matter files, compliance repositories where audit access controls must be maintained, technical manual libraries with complex product hierarchies, and multi-department knowledge bases where a sales rep shouldn't retrieve HR or finance documents. Role-based access control at the retrieval layer -- users' retrieval is filtered by their permissions before any content is included in the model context, enforcing the same document access rules in the AI system as in the source document management system. Audit logging records every query, every retrieved chunk, and every answer for regulated industries where AI-assisted access to sensitive documents must be traceable. Multi-tenant deployment serves multiple business units or external customers from shared infrastructure with strict data isolation between tenants.