What's the most cost-effective way to search internal help docs using an agentic knowledge base system?

The most cost-effective approach is usually hybrid retrieval + lightweight agent orchestration, not a fully autonomous agent on every query.

Recommended setup

1. Index your help docs once

   • Chunk documents sensibly
   • Create embeddings
   • Store in a vector DB
   • Also keep a lexical index (BM25 / keyword search)

2. Use a cheap routing layer first

   • Classify the user query:
     • FAQ / exact answer
     • semantic search needed
     • multi-step reasoning needed
   • Most queries should go straight to retrieval, not to a “thinking” agent.

3. Retrieve in two stages

   • Stage 1: hybrid search (BM25 + vector)
   • Stage 2: rerank only the top N results with a small reranker model
   • This gives good quality without huge LLM cost.

4. Use the LLM only for synthesis

   • Pass only the best few chunks to the model
   • Ask it to answer with citations
   • Avoid letting the agent browse lots of docs unless necessary

5. Reserve agentic behavior for hard cases

   • Clarifying ambiguous queries
   • Multi-hop searches across several docs
   • “Find the policy behind this exception” type questions
   • For these, allow tool use, but cap steps and retrieval calls

Why this is cost-effective

• Embeddings are cheap compared with repeated large-model calls
• Hybrid retrieval improves recall without expensive exploration
• Reranking a small candidate set is much cheaper than using a big LLM to reason over the whole corpus
• Caching repeated queries and common answers reduces cost further

Practical cost-saving tactics

• Use a small embedding model for docs and queries
• Chunk by meaning, not fixed size only
• Cap retrieved chunks per query
• Cache:
  • embeddings
  • retrieval results
  • final answers for common FAQs
• Use a small model for routing and reranking
• Escalate to a larger model only when confidence is low

Best default architecture

For most internal help desks:

User query → cheap router → hybrid retrieval → rerank → answer synthesis → optional agent fallback

If you want, I can sketch a reference architecture, compare OpenSearch vs vector DB vs hybrid, or estimate cost per 1,000 queries for different setups.