Retrieval-Augmented Generation

Retrieval-augmented generation names retrieve-then-generate architecture enabling current private knowledge without constant retraining. Failures split cleanly: retrieval versus generation versus governance eligibility.

Buyers should evaluate eligibility, permissions, and audit trails alongside embedding benchmarks.

Training data freezes at a point in time. Products, policies, and APIs move weekly. Retrieval-augmented generation is how general models stay current without retraining cycles that cannot match business velocity.

Architecture reviews should score eligibility and auditability equal to latency. Many production incidents trace to wrong eligibility, not weak embeddings.

Document ingestion, indexing, retrieval, context assembly, and generation with observability at each hop.

Define index freshness SLOs after source changes. SLOs align docs and platform teams.

Reassess architecture after CMS migrations, rebrands, and locale expansion.

Track embedding and index versions in traces for regression diagnosis.

Staff cross-functional ownership: search for retrieval, knowledge managers for sources, support for phrasing, legal for claims. Retrieval-augmented generation fails when it is engineer-only.

Run locale-specific eval sets when products ship internationally. English-only golden questions hide retrieval failures customers see daily.

Nintendo docs assistant uses retrieval-augmented generation over the Nintendo API Documentation. A developer asks how to rotate API keys. Retrieval finds the authentication guide section on key rotation, not the unrelated webhook page that also mentions keys.

The assistant returns steps, cites the doc section, and respects the public-safe boundary. Internal admin procedures stay out of retrieval because they live in a separate collection blocked for external docs search.