Prompt

I'm building a chatbot for employees that needs to answer from approved sources only. What architecture should I use?

Artificial Intelligence / AI Search1 observationLast seen Oct 10, 2026

Latest observation

Oct 10, 2026OpenAI APIWeb search: off

Use a retrieval-augmented generation (RAG) architecture with strict source grounding and access control.

Recommended architecture

  1. Approved source ingestion

    • Collect only sanctioned documents, wikis, policy pages, helpdesk articles, etc.
    • Normalize them into chunks with metadata:
      • source
      • title
      • URL/path
      • last updated
      • department
      • permission level
  2. Indexing layer

    • Build a search index:
      • vector index for semantic retrieval
      • optionally keyword/BM25 for exact policy terms and names
    • Store metadata separately so you can filter by source/permission.
  3. Query-time retrieval

    • Before generation, retrieve only from approved sources.
    • Apply:
      • document-level permissions
      • source allowlists
      • freshness filters
    • Retrieve top-k passages and pass them to the model.
  4. Answer generation with citations

    • The LLM should answer only from retrieved context.
    • Require inline citations or footnotes to the exact source chunks.
    • If evidence is insufficient, the model must say:
      • “I don’t know based on the approved sources.”
  5. Guardrails

    • Add a policy layer that:
      • blocks answers without citations
      • rejects unsupported claims
      • prevents use of external web data unless explicitly approved
    • Use a refusal template for out-of-scope or insufficient-evidence queries.
  6. Audit and logging

    • Log:
      • query
      • retrieved documents
      • final answer
      • citations used
    • This helps with compliance and debugging.

Best practice pattern

A good pattern is:

User question → policy/permission check → retrieve from approved corpus → rerank → LLM answers with citations → post-check for groundedness → return or refuse

Key design choices

  • Do not let the model browse freely unless that web content is also approved.
  • Do not rely on the LLM’s memory for policy or employee info.
  • Use metadata filters and ACLs so users only see what they’re allowed to see.
  • Prefer “answer only with citations” for compliance-heavy environments.

If you want the simplest safe version

Start with:

  • a single approved document store
  • hybrid search
  • a grounded-answer prompt
  • citation requirement
  • refusal if retrieval confidence is low

If you want, I can sketch a reference architecture diagram or propose a tech stack for this setup.

Observation history

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.

Which AI systems does Obsurfable collect answers from?

OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, Meta AI, Qwen.

How can brands improve their AEO?

Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.

Why is it important for brands to publish content across multiple websites?

Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.

Want this interpreted for your brand?

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