u/bonsaisushi

If you paste transcripts into chatgpt/claude or any other "chatbot", you have probably watched it turn one participant into "users" and state it with total confidence.

The thing that fixed it for me was refusing to accept a finding I could not trace. For every AI-assisted claim I keep the chain visible:

- raw data (the actual quote or clip)

- observation (what was said or done)

- interpretation (what I think it means)

- recommendation (what we should do)

If I can see all four for a claim, it is reviewable. If they collapse into one fluent paragraph, I have a nice artifact and no research. I also tag a confidence level and a data class on each, so consent and retention travel with the evidence.

Side effect: it stops the model leaping to a conclusion before the evidence is checked, because the prompt forces each layer out separately.

Curious how others keep synthesis honest once AI is in the loop.

Sharing a pattern that cut our cloud exposure without hurting answer quality.

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Instead of a global "use local" or "use cloud" switch, we classify each request and route it:

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- The classifier reads the user text AND the retrieved context, not the prompt alone. A clean-looking question that pulls a confidential chunk is a confidential request.

- A policy gate maps data class to allowed routes. Restricted data stays local-only (llama.cpp / Ollama / vLLM depending on the box). Internal goes to an approved business API. Public can use a general cloud route.

- Every answer carries a trace: data class, model, endpoint, retention profile, source IDs. When someone asks "did a cloud model see this ticket", the trace answers instead of a shrug.

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The part that surprised me: most of the privacy win came from classifying retrieved context, not the prompt. Pasted logs and identifiers are where clean routing leaks.

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Happy to go deeper on the classifier design or the local runtime choices if useful.