Ingestion & Pre-processing
Connect to file stores, wikis, ticketing systems, PDFs, databases, and APIs. Clean the corpus with deduplication, boilerplate removal, OCR where needed, and language detection. Split content into chunks typically fixed sizes of 500–1,000 tokens with overlaps—and, when helpful, use hierarchical splitting (section → paragraph → sentence) so retrieval can operate at multiple granularities. Apply redaction and classification to remove or label PII and secrets before anything is indexed.

Indexing
Build a sparse index (e.g., BM25) for lexical matches and a dense index with embeddings for semantic matches, using ANN structures such as HNSW or IVF-PQ for speed at scale. A hybrid index merges signals from both. Optionally, add late-interaction models (e.g., ColBERT-style multi-vector indexing) to capture precise term-level matching without losing semantic recall.

Retrieval
Fetch top-K results from one or more indices while enforcing filters such as ACLs, metadata constraints, and time windows. For higher precision at the top of the list, apply cross-encoder re-ranking to refine the initial candidate set and improve precision@K.

Post-retrieval Processing
Compress context by extracting key sentences, performing map-reduce summaries, or selecting quotable spans. Remove near-duplicates and promote result diversity so the final context window covers distinct, corroborating sources.

Generation
Use prompt templates that enforce grounded answers with citations and the rule to “only use provided context.” Produce outputs in the required structure—JSON, bullet points, or prose—and apply guardrails appropriate to the domain.

Observability & Governance
Log queries, retrieved document IDs, answers, citations, user identifiers, and timestamps. Track metrics such as retrieval recall, groundedness, latency, cost, and policy compliance. Maintain rollbacks and versioned indices, and record data lineage to support audits and reliable incident response.