Post-training teaches a model to answer the way its examples answer, so the examples have to come from people who know the answer. Kili puts your specialists in charge of writing them, measures how well they agree before anything is exported, and runs wherever your data must stay.
Every handoff between a specialist and the training file is a place where their judgment gets paraphrased. In Kili, your specialists write and correct the examples themselves, starting from the model's own draft where there is one, with no spreadsheet, script or config file between them and the dataset.
A mistake in an evaluation set costs you a wrong score. A mistake in training data gets learned and repeated. Every expert-written example can pass a second specialist before export, nobody approves their own work, and where several experts judge the same response, Kili scores how far they agree.
When a fine-tuned model picks up a habit you didn't want, you need the examples that taught it, and who wrote them. In Kili, each example keeps its history: the model's draft as imported, the expert's rewrite and the reviewer's approval, each a version with a name and a time, retrievable through the SDK.
Post-training is a loop, not a delivery: experts write, the model retrains, experts check the next version. Every hop across a network boundary slows that loop and adds a security review. Run Kili on your own Kubernetes, next to your training stack, and export straight into your pipeline through the SDK.
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A fine-tuned model imitates its examples, so a plausible answer written by a generalist teaches plausible answers. The model's draft arrives pre-filled beside the prompt, your specialist rewrites it into the answer they would sign, and both versions are kept, so the rewrite trains the model and the pair shows what the expert changed.
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Reinforcement learning with verifiable rewards only works if every problem has an answer a program can check, and writing those problems takes a specialist. Your experts write each problem and its reference answer, a Python plugin runs your checks every time they submit and sends failures back with the reason attached, and a second expert reviews what passes.
See how Kili works for your use casePreference training learns from which answer your experts would rather ship, and why. In Kili's experimental LLM projects, specialists rank the responses of two or more models, rate single turns as well as whole conversations, and write a better answer when neither response is good enough, so every judgment becomes a training signal.
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Once your specialists work in Kili, the next job doesn't need another tool. They can evaluate the model they just trained, or label the documents, images, video, audio or geospatial data your next model learns from, in new projects with the same review steps, roles and deployment your security team already signed off.
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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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