Reducing hallucination is a configuration discipline, not a single switch. The steps below apply whether you are building a Prompt Builder template, an Agentforce topic, or a Service Cloud Einstein feature.
- Identify the surface and the stakes
Decide where the GenAI output lands: agent console suggestion, customer-facing chat, auto-sent email, autonomous action. The stakes determine how aggressive your guardrails must be.
- Turn on Einstein Trust Layer features
Setup, Einstein, Trust Layer. Enable data masking, toxicity detection, prompt and response auditing. These are baseline, not optional.
- Ground the prompt against authoritative data
In Prompt Builder, add a grounding step that pulls the record, related records, knowledge articles, or Data Cloud entities into the prompt. Test with records that have thin context to see how the model behaves when grounding is sparse.
- Require structured output and citations
Use the output schema to constrain free-text fields. Require a citation field for any factual claim. Validate citations post-generation against the source.
- Set up a weekly review sample
Pick 50 to 100 responses per week. Grade them. Track hallucination rate over time. Tie regressions to template versions or data changes.
Records, related records, knowledge, Data Cloud entities, files. Add only what the task needs. Stuffing the context window dilutes the relevant context.
For features that expose a score (Einstein Case Classification, Einstein Article Recommendations), set a threshold below which the suggestion is suppressed instead of shown.
Require the model to return a source identifier with each claim. Reject responses without citations at the template layer.
Use Prompt Builder structured output. Define fields, types, and enums. Free-text fields hallucinate more than enums.
Capture thumbs up and thumbs down on every response. Tie feedback to prompt template version. Use the data to retrain templates, not just to monitor.
- Grounding reduces hallucination, it does not eliminate it. Models can still misread or recombine grounded facts incorrectly.
- A model is most confident when it has partial context. Half a fact is more dangerous than no fact. Design prompts to abstain when grounding is sparse.
- Hallucination rate drifts. A template that passed review at launch can degrade as knowledge articles change. Sample weekly, not once.
- Vague prompts produce vague, confident outputs. The model fills in the missing specificity. Pin down task, audience, and format in the template.
- Trust Layer logs prompts and responses but does not flag hallucinations automatically. Set up your own review loop.