Case Study — AI Optimisation
The Brief
Turing builds the infrastructure that makes AI models smarter. At the core of that infrastructure is human feedback — carefully designed tasks, rigorously evaluated responses, and the judgment calls that no algorithm can make yet.
As a contractor team lead, the work was to design and refine the prompt-and-response tasks that frontier LLM agents use for reinforcement learning from human feedback. Every task had to be precise enough to produce a clean training signal. Every evaluation had to be consistent enough to scale.
The Process
Each project followed a disciplined cycle: design the task, stress-test it against edge cases, refine the evaluation criteria, then execute at volume. The goal was never speed alone — it was a consistent signal the model could actually learn from.
Every prompt started as a design problem. What behaviour should the model learn? What does a correct response look like versus a plausible-but-wrong one? The task had to be tight enough to produce a clean training signal.
Before any task went to the team, it was stress-tested. Where could a model give a technically correct but misleading answer? Where were the evaluation criteria ambiguous? Every edge case caught in design is a corrupted data point prevented at scale.
With the task locked and rubric clear, execution ran at volume. Quality checks ran continuously — not at the end. A sub-15% error rate across 8,000+ prompts isn't a result of luck; it's the output of a process that caught drift before it compounded.
The Scale
Across multiple concurrent projects and multiple frontier models, the throughput stayed high and the quality held. That combination — scale without quality degradation — is the actual hard problem in RLHF work. It requires systems thinking as much as domain expertise.
The Outcome
Over 8,000 prompt-and-response tasks designed, evaluated, and delivered across multiple projects — each one contributing directly to the training signal for frontier LLM agents.
A sub-15% error rate maintained consistently across all projects. In RLHF work, data quality isn't a nice-to-have — a corrupted training signal compounds at scale. The threshold held.
Multiple concurrent projects, multiple model types, one consistent standard of output. Managing quality across parallel workstreams without drift is a systems and leadership problem as much as a technical one.
The downstream output of this work — better-trained frontier models — is the measure that matters. Every task that cleared QA fed directly into model improvement cycles at one of the leading AI labs.
What this signals
The same thinking that goes into building a brand system — clarity of objective, consistency of execution, quality that scales — is what makes RLHF work produce a usable training signal. The domain changes. The discipline doesn't.