How do I see whether answer engines are using my docs or skipping them?

To tell whether an answer engine is using your docs or bypassing them, you want to check evidence of retrieval and evidence of grounding.

What to look for

1. Retrieval logs

If the system has RAG-style logging, inspect:

• which documents/chunks were retrieved
• retrieval scores
• whether retrieval happened at all for a query
• how many chunks were injected into the prompt

If your docs are being used, you should see:

• relevant chunks returned
• citations or source IDs from your docs
• prompt context containing your content

If they’re being skipped, you may see:

• no retrieval step
• low/empty retrieval results
• generic answers with no source references
• the model answering from prior knowledge only

2. Citations / source traces

Many answer engines can show:

• inline citations
• “sources used”
• footnotes or linked doc excerpts

Check whether the final answer cites your documents, especially for questions that should be answered only by your docs.

3. Prompt inspection

If you can inspect the exact prompt sent to the model, verify:

• retrieved passages are included
• the instructions explicitly require using the provided context
• the answer is not being generated before retrieval finishes

This is one of the best ways to tell whether docs are actually being passed in.

4. Controlled test questions

Ask questions where the answer exists only in your docs, for example:

• a unique policy detail
• a product-specific term
• a private internal procedure

Then compare:

• answer accuracy
• whether citations point to the right doc
• whether the engine hallucinates a plausible but wrong answer

If it answers correctly without citing your doc, it may still be using the doc silently, but if it gets the unique detail wrong, it’s likely skipping or retrieving poorly.

5. Chunk-level debugging

If you manage the retrieval pipeline, check:

• chunking settings
• embedding model
• index freshness
• metadata filters
• top-k retrieval count

A lot of “skipping” is actually:

• bad chunking
• stale index
• too-small top-k
• metadata filter excluding the docs
• query rewriting changing the meaning

A simple checklist

For a given query, confirm:

1. Did retrieval run?
2. Were any of my docs returned?
3. Were the returned chunks relevant?
4. Were those chunks inserted into the prompt?
5. Did the final answer cite them?

If any step is missing, the engine may be bypassing your docs.

Practical indicators of “skipping”

• No citations, even when citations are expected
• Answers are generic and uncoupled from your wording
• The model says “I don’t have access to that” when it should
• Retrieval logs show zero or irrelevant hits
• Answers stay the same after changing the docs

Practical indicators of “using your docs”

• Answers contain exact terminology from your docs
• Citations point to your files/sections
• Retrieved chunk text appears in logs or trace
• Answer changes when you update the docs
• Questions with doc-only facts are answered correctly

If you want, I can help you build a debug checklist for a specific answer engine like ChatGPT with custom GPTs, Perplexity, Claude, Azure AI Search, LangChain, or LlamaIndex.