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Once you have a working KB, retrieval quality has three knobs: what you index, how you chunk it, and how many chunks you pull at query time.

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The three levers

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1. What you index

Garbage in, garbage out. Before tuning chunking or top_n, look at the actual chunks being retrieved (KB → source → view chunks) and ask:
  • Is there boilerplate (nav, footers, copyright notices) competing with the real content?
  • Are there outdated articles still in the index?
  • Is the same fact present in multiple slightly-different versions, splitting the retrieval signal?
Cleaning up sources usually moves the needle more than any other tuning.

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2. How you chunk

See Chunking and embeddings. Quick guide:
  • Switch to structure-based for any source where answers naturally live inside a section or list.
  • Lower the chunk size (e.g. 500 chars) if your content is dense and you want more focused retrieval.
  • Raise the chunk size (e.g. 1500 chars) if answers span multiple sentences and the LLM keeps missing context.

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3. How many chunks (top_n)

Default is 5. Adjustments:
top_nUse when
3Content is highly precise, you want the LLM not to be distracted by lower-ranked matches
5 (default)Most use cases
8-10Answers often require context from multiple chunks (e.g. a process spread over several sections)
15+Rarely useful; LLM starts to lose focus
Set top_n per agent attachment, or per explicit knowledge_base tool.

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Testing retrieval directly

Use the API to test queries without involving an agent: 
curl -X POST https://api.anyreach.ai/knowledge-base/datasets/$DATASET_ID/query \
  -H "Authorization: Bearer $ANYREACH_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "what is the warranty period?",
    "results_count": 5
  }'
The response includes the matching chunks and their similarity scores. Use this to:
  • Validate retrieval quality outside the agent loop
  • Build automated regression tests for KB changes
  • Wire KB queries into workflows directly via an HTTP step

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Adding rewrites for hard cases

If callers ask a question in many different phrasings, add an FAQ-style rewrite source:
Q: How long is the warranty? A: All products carry a 2-year limited warranty from the purchase date. See [warranty.md] for full details.
This dense Q&A chunk will match casual phrasings (“how long do I get to return”, “is there a warranty”, “what about defects”) much better than the original policy doc. A small “FAQ rewrites” source with 50-100 high-traffic Q&As often outperforms ten times the volume of original docs.

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Multi-KB strategies

When content spans clearly distinct domains, splitting into multiple KBs and attaching all of them to one agent works better than one large KB:
  • The agent retrieves from each KB with the same top_n
  • Each KB has more focused embeddings (less semantic crowding)
  • You can swap one KB’s content without recomputing others
Use this when domains are unambiguous (Product / Policy / Pricing). Don’t use this if the LLM would have to guess which KB to draw from for a single question — it doesn’t choose, both are queried.

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When tuning isn’t enough

If you’ve tuned top_n, switched chunking, cleaned up sources, and still miss key answers, the underlying problem is usually:
  1. The answer literally isn’t in the source content. Add it.
  2. The query’s phrasing is so different from the doc that no embedding model bridges the gap. Add Q&A rewrites.
  3. Your agent’s prompt is suppressing KB-grounded answers. Check the system prompt for instructions like “answer concisely” — the LLM may be skipping context to be brief.