Neel Patel

A foundational approach to making prompts reliable, auditable and repeatable

We spend a great deal of time on models and data. The instructions we give those systems, the prompts, receive far less attention. That matters. A poorly specified prompt can produce unpredictable outputs, waste time and raise risks in use cases where its performance is driving real world results. Treating prompt quality as an engineering problem keeps the approach simple and practical. This short essay outlines the foundations; the preprint contains the full paper.

Prompt quality is the extent to which a prompt leads to outputs that are accurate, relevant and aligned with intent. The scope here is the prompt itself, not model internals or how someone later interprets the output. A good prompt is explicit, repeatable and versionable. It should state expected behaviour clearly enough that teams can test for it and assign responsibility when outcomes are low.

To keep this practical, prompt quality is usefully viewed through two complementary lenses. The first is the human facing, linguistic side. Prompts should be clear, avoid ambiguity, and use precise constraints when needed. They should supply the context and examples required for the task, keep multi part instructions organised, and use a tone suited to the audience. These attributes guide how prompts are written.

The second lens is operational. Prompts must behave reasonably in production. That means keeping an eye on token use, latency and cost. It means designing prompts that can be modularly reused and version controlled. It means resisting simple attacks or accidental misuse, and avoiding phrasing that increases the risk of exposing sensitive information. These attributes guide how prompts are managed.

Practical habits keep the work small and useful. Keep prompt versions and a short change history. Attach a one paragraph intent note to every customer facing prompt. Maintain a small, repeatable test suite and run it when model versions change. Route high impact prompts through a brief human review. Monitor token usage and flag sudden increases. These practices are deliberately low friction; they address common failures without creating heavy process overhead.

Prompts appear in different flows and the quality checks should match those flows. When humans interact directly with a model, prioritise clarity and robustness to rephrasing. When one system supplies input to another, prioritise format consistency and mutation detection. When prompts pass through multiple systems, prioritise preservation of intent and traceability across stages. It helps to think of the interaction as a simple graph: agents as nodes, prompt transfers as edges. For each transfer, record version and transformation details and attach an appropriate QA check.

This is not a promise of perfect outputs. It is a modest, practical proposition: treat prompts as engineering artefacts. Document them, test them, version them and gate the ones that matter. Those habits reduce surprises and make outcomes easier to explain. For a full account of the framework, measurement approaches and process pillars see the preprint: https://doi.org/10.5281/zenodo.17316665