TrustGuide5 min

Add citations and evidence to AI-generated recommendations

How to make AI recommendations easier to trust, dispute, and improve inside operational software.

Real example

Example: evidence-backed churn-risk recommendation

An AI assistant says a customer is high churn risk. The account manager needs to know whether that comes from ticket sentiment, usage drop, billing issue, or missing onboarding.

Require every risk factor to include a source id, source type, and short evidence summary. The UI renders those sources beside the recommendation.

The account manager can trust, challenge, or correct the recommendation quickly.

Tutorial path

How to implement it

Step 01

Require each recommendation to include supporting evidence IDs or snippets.

Step 02

Render evidence next to the recommendation, not behind a separate debug panel.

Step 03

Flag unsupported recommendations and route them to manual review.

Step 04

Let reviewers mark evidence as useful, wrong, stale, or insufficient.

Step 05

Use those review labels to improve retrieval and prompt instructions.

Checklist

Ready when these are true

Evidence field required

Source links visible

Unsupported output blocked

Reviewer evidence feedback

Facts and judgments separated

Field notes

What matters in practice

Recommendations without evidence create review burden instead of reducing it.

Evidence should point to source records, document sections, or tool results.

The UI should separate facts from the model's recommendation.

Avoid these mistakes

Common failure modes

Do not cite sources that were not actually retrieved.

Do not bury evidence in a debug drawer.

Do not mix evidence and model opinion in the same sentence.

Practical tip

Evidence is part of the product UX. If users cannot see it, they cannot calibrate trust.

Apply this to a build