Grounded

Problem

Document Q&A that answers from a model's memory can't be trusted in a certification-learning business. Answers had to come only from curated source content, with citations a reader can check — and ingestion had to be manageable through an admin surface, not a deploy.

What I built

• A monorepo split by audience: a reader panel, a login-gated ingestion admin, and one backend serving both.
• An ingestion pipeline: text extraction, chunking with overlap, and embedding into a vector knowledge base through the admin panel.
• Retrieval through a single matching function, so the query path has one place to tune and one place to audit.
• Generation constrained to numbered retrieved passages, with every claim carrying its citation.
• Multimodal grounding end to end: image captions drive retrieval while the image itself travels as an inline part.

Technical decisions & trade-offs

• Embedding and retrieval sit behind the database layer rather than in the client, so the ingestion format and the query path can change without touching either frontend.
• The model key lives in Edge Function secrets, so no model call ever originates in a client bundle.
• Row-level security grants anonymous read and admin-only write, and the service-role decision lives in one auditable module rather than smeared across handlers.
• Grounding is enforced structurally: the model only ever sees retrieved passages, so ungrounded answers cannot happen rather than being discouraged.
• Two frontends over one backend instead of one app with role switches, so the reader surface stays anonymous and cacheable.

Challenges solved

• Chunk-boundary tuning: enough overlap to keep answers coherent across boundaries, without bloating the context sent to the model.
• Multimodal grounding — captions retrievable as text while the source image rides along inline, so citations stay meaningful for figures.

Outcome

In production. Every sentence traces to a cited passage and ungrounded output is structurally impossible. The pattern became the reference architecture for retrieval work on the team.