A fluent answer is not a correct one, and only your specialists can tell the difference. In Kili they evaluate LLM outputs, write and correct the examples your model is post-trained on, and annotate the text behind it, while Kili measures how far they agree.
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Your model is judged by the people who can tell a correct answer from a convincing one. Lawyers, clinicians and analysts grade outputs against your criteria and correct the model's pre-annotations themselves, with no code and no engineer in the loop. Answers that sound right but aren't get caught before your users see them.
Every judgment your experts make can feed the next model version. In Kili's experimental LLM projects, they rank responses from two or more models, rate single turns and whole conversations, and write a better answer when none is good enough. Conversations traced from production go through the same review, so real failures become training signal.
You can show how reliable your evaluation and post-training data is, not just assume it. Consensus scores how far experts agree, Honeypot checks them against answers you trust, and a second reviewer signs off. Text stays in your own cloud bucket while you use Kili's cloud, or Kili runs on your own Kubernetes.
Speak to our team for a free 14-day evaluation.
Kili Technology is a complete data suite that supports all data types and handles specialized formats for domain-specific requirements.
Customer stories from the programs that put this to work — with the numbers behind them.
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Got questions about Kili Technology? Check out our FAQ. If you can't find it in this list, drop a question for our team.
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