rag-architect

Retrieval-augmented generation grounds LLM outputs in factual knowledge by designing vector stores, selecting embeddings, chunking documents with metadata, and implementing semantic or hybrid search pipelines. This architecture retrieves relevant context before generation, improving accuracy for chatbots, Q&A systems, and knowledge bases at scale.

Vector store design requires selecting a database that supports your scale and query patterns, defining schemas for embeddings and metadata, and planning ingestion workflows. Consider latency requirements, multi-tenant isolation, and reranking capabilities to ensure retrieval performance matches your production workload.

Hybrid search combines vector similarity search with traditional keyword matching to improve recall and relevance. Implement both retrieval paths in parallel, normalize scores, and optionally apply reranking to surface the most relevant results before passing context to the LLM.

Yes. Measure retrieval precision, latency, and ranking effectiveness using standard metrics. Monitor embedding performance, track ingestion idempotence, and version both models and embeddings to ensure consistent quality across updates and identify degradation.

Document chunking preserves semantic boundaries while fitting embedding token limits. Enrich chunks with metadata—source, timestamp, section hierarchy—to enable filtering, multi-tenant queries, and contextual reranking, improving both retrieval precision and downstream LLM grounding.

Multi-tenant RAG requires metadata-based isolation, tenant-scoped ingestion workflows, and query filtering to prevent cross-tenant data leakage. Design schemas and retrieval pipelines with tenant context throughout embedding, search, and reranking stages.