Knowledge Bases for LLM Apps: A Build Guide

An LLM over your knowledge base sounds simple — upload files, ask questions — but the quality of the answer is decided long before the model runs. Chunking, embeddings, retrieval, and refresh strategy determine whether your app finds the right paragraph and cites it correctly, or confidently invents an answer from a mismatched chunk.

Chunking strategy

Chunks are what retrieval matches against. Too large and the relevant signal drowns in noise; too small and the chunk misses the sentence that answers the question. In practice, split on semantic boundaries — sections, paragraphs, list items — rather than fixed character counts. Keep context-losing fragments whole: a table, a code block, a numbered list. Aim for chunks that answer a single question, and keep a little overlap when boundaries force awkward splits.

Choosing an embedding model

• Match embedding size to your scale and latency budget.
• Prefer a model fine-tuned on your document type if one exists.
• Compare on your own retrieval task, not a public benchmark.
• Normalize inputs and watch for low-cardinality domains like names, SKUs, and acronyms.
• Re-embed on model upgrades — old and new vectors do not mix well.

Retrieval quality

Retrieval quality is a ranking problem. Start with top-k by cosine similarity, then add metadata filters — document type, date, department — so irrelevant sections never compete. Hybrid search that blends keyword match with embeddings catches exact terms that vectors miss. Test with the same question in ten phrasings and measure how often the right chunk ranks in the top five; that number, not the demo, is your retrieval score.

Answering and citations

1. Retrieve the top k chunks and pass only those as context.
2. Instruct the model to answer strictly from the provided chunks.
3. Require citations to the source chunk for every claim.
4. Add a not-in-the-context escape hatch instead of forcing an answer.
5. Re-rank with a lightweight model if top-k precision matters.

Keeping the knowledge base current

The knowledge base decays the moment you stop updating it. Index on document change rather than a nightly cron: listen to the content store, delete replaced chunks, re-embed edited ones, and keep a version stamp per chunk. Your cache and retrieval layers must honor the same invalidation, or you will serve deleted policies for weeks. Publish a last-updated date and let users see it — it sets honest expectations.

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