Hallucinations often follow a diagnosable pattern involving prompts, context, retrieval, tools, data, or model behavior. Investigate the evidence, fix the supported cause, and verify it in production.
Guardrails can block, flag, or route risky generated content before it propagates, so they remain useful defense in depth. By themselves, they do not repair the prompt, retrieval, tool, data, or model condition that produced it.
Lower temperature can reduce variation, but it does not supply missing evidence, resolve ambiguous instructions, or guarantee factual output.
A generic instruction does not supply missing knowledge or repair retrieval, tool, data, and model limitations. It needs grounding evidence and an uncertainty behavior to be actionable.
These are recurring, diagnosable contributors — not an exhaustive taxonomy. Confirm the evidence before selecting a targeted fix.
Conflicting or vague directives can push a model to fill gaps with plausible-sounding content instead of grounding the response in available evidence.
When required information is absent, an agent may still produce a confident-sounding answer instead of expressing uncertainty or requesting context.
RAG can retrieve the wrong document or passage, and the agent may treat irrelevant retrieved content as authoritative.
Vague tool descriptions can lead to incorrect tool selection or parameters, injecting missing or incorrect data into the response path.
Find conversations where the agent generated inaccurate information. Cluster by type: fabricated facts, incorrect data, made-up procedures.
Turn-level diagnosis identifies where the inaccurate output first appeared and tests prompt, context, retrieval, tool, data, and model evidence before assigning a cause.
A fix specific to the source - clarified instructions, added knowledge, fixed retrieval, improved tool descriptions. Tested in simulation against the original scenario and similar cases.
Measure the hallucination rate for this specific pattern before and after deployment. Marked verified, not fixed, or confounded - no guessing.
Confident false or internally inconsistent outputs can arise from several interacting factors, including ambiguous instructions, missing context, retrieval failures, tool configuration, data quality, and model behavior. The four examples on this page are common contributors to investigate, not an exhaustive taxonomy.
Turn-level diagnosis identifies where an inaccurate output first appears, then tests the active instructions, available context, retrieval, tool calls, and other evidence at that point. It should name a prompt segment only when the causal evidence supports that attribution.
Guardrails and grounding checks can block, flag, quarantine, or route generated content before it reaches a user, memory store, or downstream agent. They reduce propagation risk but do not necessarily repair the condition that produced the output, so pair them with a scoped root-cause fix and ongoing measurement.
Simulation testing with synthetic personas that specifically probe the scenarios that triggered hallucinations. The fix is tested against the original failure case plus similar cases to ensure it resolves the issue without introducing new hallucinations elsewhere. Converra automates this testing and adds regression protection.
Production verification measures the hallucination rate for the specific pattern before and after the fix deploys. The fix is marked as verified (hallucination rate dropped), not fixed (rate unchanged), or confounded (other variables changed simultaneously). This closes the loop - no guessing.
Connect your agent and see which prompt segments, context gaps, or retrieval issues cause hallucinations - then fix them automatically.
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