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Launch HN: Mentat (YC F24) – Controlling LLMs with Runtime Intervention

The prompt

Hi HN, I’m Cyril from CTGT. Today we’re launching Mentat ( an API that gives developers deterministic control over LLM behavior, steering reasoning and removing bias on the fly, without the compute of fine-tuning or the brittleness of prompt engineering. We use feature-level intervention and graph-based verification to fix hallucinations and enforce policies. This resonates in highly regulated industries or otherwise risky applications of AI where the fallout from incorrect or underperforming output can be significant. In financial services, using GenAI to scan for noncompliant communications can be arduous without an easy way to embed complex policies into the model. Similarly, a media outlet might want to scale AI-generated summaries of their content, but reliability and accuracy is paramount. These are both applications where Fortune 500 companies have utilized our technology to improve subpar performance from existing models, and we want to bring this capability to more people. Here’s a quick 2-minute demo video showing the process: Standard "guardrails" like RAG and system prompts are fundamentally probabilistic: you are essentially asking the model nicely to behave. This often fails in two ways. First, RAG solves knowledge availability but not integration. In our benchmarks, a model given context that "Lerwick is 228 miles SE of Tórshavn" failed to answer "What is 228 miles NW of Lerwick?" because it couldn't perform the spatial inversion. Second, prompt engineering is brittle because it fights against the model's pre-training priors. For example, on the TruthfulQA benchmark, base models fail ~80% of the time because they mimic common misconceptions found on the internet (e.g. "chameleons change color for camouflage"). We found that we could literally turn up the feature for "skeptical reasoning" to make the model ignore the popular myt

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