It's not easy trying to get an LLM to consistently be honest. Swimm CTO breaks down how to prompt an LLM to get the right answer and why this works with LLMs.

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Swimm's CTO shares lessons from building a semantic equivalence checker for AI-generated functional specs. Initial attempts using direct LLM comparison achieved 97% accuracy but failed on long specs where requirements were missed. Breaking down the task paradoxically worsened results to 46% accuracy, with the LLM hallucinating matches. The breakthrough came from forcing structured outputs: requiring specific evidence (matching IDs, quoted text) before conclusions, adding explicit verdict categories, and ordering the response to demand proof before judgment. This approach eliminated hallucinations by making lying harder than telling the truth, demonstrating that LLM reliability depends on designing response structures that require verifiable grounding.

To trust an LLM, make lying harder than telling the truth

The LLM Said It Found a Match. It Was Lying.

Validating the Semantic Equivalence Checker

Stepping Back: When Did the LLM Lie? What Can It Teach Us?

The Key Insight: LLMs Don’t Lie When Cornered with Specificity

<p>This really got to me, and the lesson “make lying harder than telling the truth” is excellent. I like how you transformed prompt design into a real accountability system for llms, that’s the kind of rigorousness that ai needs at the moment.</p>
