Methodology · Section 01
The Requirements Confidence Framework
A working framework for closing the gap between “the AI built something” and “the AI built the thing you actually asked for.”
Why this exists
Modern AI coding agents are extraordinary at the first 80% of a build. They can assemble plausible structure, fluent code, sensible patterns, faster than any team. And then they stop being useful right where it matters most: when a real product needs a real shape, real behaviour, and real evidence that the shape and the behaviour are what was actually requested.
RCF is the discipline you bring to bear at the boundary, where intent has to become specification, where specification has to become behaviour, and where behaviour has to be tested against the thing the operator originally meant. Done right, the framework gives you confidence not in the AI’s output, but in the chain from intent to delivered behaviour.
What it covers
The framework lives in five reinforcing parts: the methodology itself, the tooling that operationalises it, the role of an RCF-capable engineer, a library of build patterns that emerge from running it on real software, and the lifecycle architecture (tests, deploys, observability) that supports the whole thing in production.
How to read this section
This is the entry point. Deeper sections, on the why in detail, the mechanics of each part, and worked examples from real builds, are added as the framework itself stabilises. Methodology earns its way onto the page by working in practice first.
Next, when ready: The five parts in detail · Worked examples · Tooling reference