Can hallucinations be completely eliminated?

No model can guarantee zero hallucinations. However, the techniques in this guide can reduce them to very low levels. The combination of RAG, strict prompting, and validation catches the vast majority of fabricated content.

Does lowering temperature make the agent less creative?

Yes, lower temperature reduces creative variation. For factual and support use cases, this is a benefit. If your agent also needs creative capabilities, consider using model switching to use different temperature settings for different task types.

How do I measure hallucination rates?

Sample 50 to 100 agent responses per week and verify them against your knowledge base. Track the percentage of responses that contain inaccurate information. Over time, this metric should trend downward as you refine your prompts and retrieval settings.

Troubleshooting

Model Response Quality: Fix Hallucinations

Reduce hallucinations and improve the factual accuracy of your OpenClaw agent's responses using grounding techniques and prompt refinements.

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What You Will Get

By the end of this guide, you will have a set of proven techniques to reduce hallucinations and improve the factual accuracy of your OpenClaw agent's responses. Your agent will give more reliable answers and clearly indicate when it does not know something.

Hallucinations occur when the model generates plausible-sounding but incorrect information. This is one of the most significant challenges in deploying AI agents, especially in domains where accuracy is critical like support, compliance, or medical information.

You will learn to identify common hallucination patterns, apply grounding techniques using your knowledge base, refine your prompts to discourage fabrication, and set up automated quality checks. The result is an agent that users can trust to provide accurate information.

Step-by-Step Troubleshooting

Follow these steps to diagnose and reduce hallucinations.

Identify Hallucination Patterns

Review recent conversations where the agent provided incorrect information. Categorize the errors: Was the agent making up facts, citing nonexistent sources, or confidently answering questions outside its knowledge? Understanding the pattern helps you choose the right fix.

Add Grounding Instructions to the Prompt

Update the system prompt to explicitly instruct the agent to base answers on provided context. Add a rule like 'Only answer based on information from the knowledge base. If the information is not available, say: I do not have that information.' This simple instruction dramatically reduces fabrication.

Enable RAG with Strict Retrieval

Configure retrieval-augmented generation with a high similarity threshold so only highly relevant documents are injected into the context. This gives the agent a factual foundation for every answer. Without RAG, the model relies entirely on its training data, which may be outdated or incomplete.

Add Citation Requirements

Instruct the agent to cite the source of its information in every response. For example, 'When referencing knowledge base content, include the document name in your response.' This forces the model to tie answers to specific sources, making fabrication harder and verification easier.

Reduce Temperature and Top-P

Lower the model's temperature and top-p settings in the agent's model configuration. Lower values produce more deterministic, focused responses that stick closer to the provided context. A temperature of 0.3 to 0.5 works well for factual tasks.

Implement Response Validation

Set up a validation step where a second, cheaper model checks the primary response for potential inaccuracies. If the validator flags an issue, the agent can rephrase or add a disclaimer. This automated quality gate catches errors before they reach the user.

Monitor and Review Regularly

Flag and review agent responses on a weekly basis. Use the conversation logs to identify new hallucination patterns. Each review cycle informs the next round of prompt and configuration improvements, creating a continuous quality loop.

Tips and Best Practices

Encourage 'I Don't Know' Responses

Agents that admit uncertainty are more trustworthy than those that always provide an answer. Reward honesty in your prompt by saying 'It is better to say you do not know than to guess.'

Keep the Knowledge Base Current

Outdated documents are a hidden cause of hallucinations. The agent may retrieve old information and present it as current. Review and update your knowledge base regularly.

Use Specific Questions for Testing

Test with questions that have verifiable answers. Compare the agent's response to the known correct answer. This gives you a clear accuracy metric rather than a subjective quality assessment.

Segment by Confidence Level

Consider having the agent indicate its confidence level. Responses based on retrieved documents can be marked as high confidence, while responses from general knowledge can be flagged as lower confidence.

Frequently Asked Questions

Advanced Prompt Engineering Memory Management and RAG Agent Personality Tuning

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