Computer Vision for Transport Monitoring

Retrieval quality is the lever that moves the most weight. No amount of prompt engineering compensates for a retriever that consistently surfaces the wrong passages. We spent two weeks tuning chunking and reranking before touching the prompt template.

What we changed

Evaluation suites grow faster than the codebase they cover. We treat them as first-class artefacts: versioned, reviewed, and regenerated on a schedule. The team that owns the model owns the eval set, not a separate QA group.

Documentation written by the team that builds the system tends to be more useful than documentation written by anyone else. The trade-off is consistency, which we address with a shared style guide and a lightweight review process.

Open questions

When the system is wrong, the user should be able to understand why in under thirty seconds. Citation links, confidence scores, and the exact retrieved passages are surfaced in the UI for every generated answer.

In production, latency distributions matter far more than averages. A pipeline whose mean response time looks acceptable can still feel sluggish if the 95th percentile drifts upward during peak hours. We instrument every stage with histograms so regressions surface immediately.

Tool definitions should read like API documentation written for a careful junior engineer. The model behaves better when each parameter has a concrete example, a unit, and an explicit statement of what happens when the value is omitted.