RAG Knowledge Systems

Consider RAG when answers need current source documents and verifiable attribution. Consider fine-tuning when prompting does not meet a defined task, style or output-format requirement and suitable training data is available.

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.

We scope hosting, access controls, redaction and logging around the data and applicable obligations. For HIPAA-covered PHI processing, establish the covered-entity and business-associate roles and a BAA where required.

Planning ranges are $40,000-$110,000 for a RAG MVP and $110,000-$280,000+ for a production system. Scope discovery separately: source connectors, document permissions, evaluation data and deployment requirements affect the estimate.

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 recommend a simpler approach when search or direct context meets the workflow requirements. Corpus size alone is not a decision rule.