Blog

Prompt tuning and fine-tuning both require labelled examples of correct behaviour. But for complex multi-step agent workflows, you often can only say whether the overall outcome was good. Reinforcement learning is exactly the right tool for that setting.

A fine-tuned 7B model trained on your domain data will outperform a frontier model on generic prompts for well-defined tasks — consistently, cheaply, and without sending your data to a third-party endpoint. Here is what the process actually involves.

Manual prompt engineering has no real feedback loop — you iterate by feel, test on a handful of examples, and hope it generalises. Genetic optimisation replaces that process with a systematic search over production traces. Here is how it works.

There are three distinct strategies for making an AI model better at a specific job. Each works differently, costs differently, and produces a different kind of asset. Here is how to choose.

Human-in-the-loop sounds safe. But it contains a structural flaw that guarantees the one genuinely dangerous decision gets the same shallow glance as the thousandth routine one.

LLMs cannot reliably report their own uncertainty — so you have to measure it from the outside. Here are the two methods that work, and when to use each.

Most AI deployments chase benchmark accuracy. But in production, value isn't destroyed by average errors — it's destroyed by the single ruinous tail event you didn't cap.

Prompt engineering is a great starting point — but at production scale, a fine-tuned 7B model running on your own infrastructure will outperform a frontier model on generic prompts every time. Here is why, and when to make the switch.