RAG Knowledge Systems

Use RAG when your knowledge base is large (>1,000 documents), changes frequently, requires citations or you need per-tenant isolation. Use fine-tuning when you have a narrow task with 10K+ labeled examples, need a specific tone or structured output that prompting cannot achieve or need to reduce inference cost at very high volume.

For small-to-medium corpora (under 10M vectors) and if you already use PostgreSQL: pgvector - simplest operations, no new infrastructure. For large-scale: Pinecone (managed, fastest to ship) or Qdrant (self-hosted, strongest for regulated data). Weaviate for native hybrid search out of the box. We avoid "custom vector database" engagements - the differentiator is rarely the store itself, it is the ingestion pipeline and retrieval strategy.

Every retrieved chunk carries metadata (source document ID, page/paragraph, title, timestamp) that flows through to the LLM prompt. The LLM is instructed via system prompt to cite the source ID for each claim.

RAG quality is measured with a two-layer evaluation: retrieval (NDCG, hit rate @k, MRR on a labeled test set of question → relevant-chunk pairs) and generation (answer correctness, citation precision, faithfulness to retrieved context). We build the eval set from real production questions (once we have them) or from domain expert annotations. Retrieval eval catches ingestion and embedding regressions; generation eval catches prompt drift and model updates. The harness runs on RAGAS or a custom judge-LLM pipeline, with regression tests on every pipeline change so accuracy does not degrade as the corpus grows.

Incremental re-embedding: new or updated documents are detected via change-data-capture (webhook or timestamp polling), re-chunked, re-embedded and upserted into the vector store. For corpora with complex permissions (Confluence, SharePoint, Google Drive), we sync permissions on every retrieval so users only see chunks they have access to.

Yes. For PHI: self-hosted embedding model (BGE-large-en, E5) and self-hosted LLM (Llama 3, Mistral) run in the client VPC; no data leaves the infrastructure.

RAG MVP $40,000-$110,000: 2 weeks discovery + eval set, 4-6 weeks build (ingestion, retrieval, generation, citations), 2-4 weeks production hardening. Production RAG with observability, monitoring and multi-model fallback: $110,000-$280,000+.

RAG (Retrieval-Augmented Generation) is a production architecture that retrieves relevant passages from your private documents or databases before an LLM generates an answer, grounding responses in current proprietary data. Businesses need it when a general-purpose LLM lacks access to internal knowledge bases, product catalogs, compliance manuals or real-time records.

Hybrid retrieval combines dense vector similarity search with sparse BM25 keyword search, then merges the ranked result lists using reciprocal rank fusion (RRF). This prevents the common failure where a dense-only system misses an exact product code or proper noun that does not embed well in the vector space.

A single-corpus RAG MVP with a REST query API, admin ingestion endpoint and basic evaluation harness typically ships in 6-10 weeks. Production deployment with monitoring, multi-tenant document access and streaming answers takes 3-5 months depending on data source complexity and required latency targets.

Yes. We audit the existing chunking strategy, embedding model, retrieval recall@k and latency profile, then issue a gap report. Common fixes include replacing naive fixed-size chunking with semantic chunking, adding re-ranking with a cross-encoder, moving from Chroma in-memory to a hosted vector store and wiring up an evaluation pipeline the prototype was missing.

We decline search use cases where Elasticsearch dominates, corpora under 100 documents (few-shot is simpler), real-time chat with sub-3-second requirements, projects without an evaluation set and projects where fine-tuning would be cheaper AND more accurate. "Let us use RAG because it is trendy" is not a use case.