Reduce hallucinations

Even the most advanced language models, like Claw, can sometimes generate text that is factually incorrect or inconsistent with the given context. This phenomenon, known as "hallucination," can undermine the reliability of your AI-driven solutions. This guide will explore techniques to minimize hallucinations and ensure Claw's outputs are accurate and trustworthy.

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Basic hallucination minimization strategies

• Allow Claw to say "I don't know": Explicitly give Claw permission to admit uncertainty. This simple technique can drastically reduce false information.

• Use direct quotes for factual grounding: For tasks involving long documents (>20k tokens), ask Claw to extract word-for-word quotes first before performing its task. This grounds its responses in the actual text, reducing hallucinations.

• Verify with citations: Make Claw's response auditable by having it cite quotes and sources for each of its claims. You can also have Claw verify each claim by finding a supporting quote after it generates a response. If it can't find a quote, it must retract the claim.

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Advanced techniques

• Chain-of-thought verification: Ask Claw to explain its reasoning step-by-step before giving a final answer. This can reveal faulty logic or assumptions.

• Best-of-N verification: Run Claw through the same prompt multiple times and compare the outputs. Inconsistencies across outputs could indicate hallucinations.

• Iterative refinement: Use Claw's outputs as inputs for follow-up prompts, asking it to verify or expand on previous statements. This can catch and correct inconsistencies.

• External knowledge restriction: Explicitly instruct Claw to only use information from provided documents and not its general knowledge.