Hallucinations aren't random - they trace to specific prompt segments, missing context, or retrieval failures. Diagnose the source, fix it, and verify it holds in production.
Guardrails catch hallucinations after they happen. They don't prevent the underlying cause. You end up with an agent that refuses to answer instead of answering correctly.
Reduces randomness but doesn't address missing context or ambiguous instructions. The agent hallucinates with more confidence at lower temperatures.
The model doesn't know it's hallucinating. This instruction doesn't address the root cause - ambiguous context, missing knowledge, or retrieval failures persist regardless.
Every hallucination traces back to one of these sources. Each one has a specific fix - not a generic workaround.
When the prompt gives conflicting or vague directives, the model fills gaps with plausible-sounding content. The more ambiguity, the more the model invents.
The agent doesn't have the information it needs to answer correctly. Instead of saying 'I don't know,' it generates a confident-sounding answer from its training data.
RAG retrieves the wrong documents, or the right documents with the wrong sections highlighted. The agent treats irrelevant retrieved content as authoritative.
When tool descriptions are vague, the model makes assumptions about what parameters to pass or when to use the tool, leading to incorrect data being injected into responses.
Find conversations where the agent generated inaccurate information. Cluster by type: fabricated facts, incorrect data, made-up procedures.
Turn-level diagnosis identifies which turn the hallucination originated and what caused it - ambiguous instructions, missing context, retrieval failure, or tool misuse.
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.
Agents hallucinate when they lack the information to answer correctly but are prompted (or inclined) to provide an answer anyway. The root causes are specific and diagnosable: ambiguous instructions, missing context in the knowledge base, retrieval failures in RAG pipelines, or overly broad tool descriptions. Each cause has a targeted fix.
Turn-level diagnosis identifies the exact conversation turn where the hallucination originated, then traces it back to the active prompt instructions and available context at that turn. This shows whether the agent hallucinated because the prompt was ambiguous, the context was missing, the retrieval was wrong, or the tool was misconfigured.
Guardrails detect hallucinations after generation - they're a safety net, not a fix. To prevent hallucinations, you need to address the source: clarify ambiguous prompt instructions, ensure the knowledge base covers the topic, fix retrieval relevance, or improve tool descriptions. Guardrails plus root cause fixes is the most robust approach.
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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