Middle Lane in AI Book, Using AI with Judgment

Inspiration BoundaryLens grew from my work on Middle Lane in AI, a practical book about using AI without hype, fear, or blind trust. Through months of evaluating AI conversations, I repeatedly saw the same problem: an answer could sound confident even when its evidence was incomplete, inferred, or missing. That led to the governing principle behind BoundaryLens: Certainty shall never exceed supporting evidence.

What it does BoundaryLens audits written claims for evidence integrity. It separates individual claims, identifies what is directly supported, flags unsupported certainty and missing verification, assigns an integrity score, identifies where human review is required, and produces a more carefully calibrated rewrite.

The goal is not merely to label an answer “right” or “wrong.” It is to show why a statement is or is not defensible.

How I built it I designed the evaluation framework and translated it into a working browser-based prototype using Node.js, JavaScript, HTML/CSS, and the OpenAI API. GPT-5.6 produces structured audit results that the interface organizes into claims, evidence gaps, risk levels, human-review flags, scoring, and calibrated rewrites.

The prototype runs locally and successfully performs live evidence-integrity audits.

Challenges The largest challenge was turning a governance concept into a consistent technical workflow. I also had to learn unfamiliar development tools, including Terminal, local servers, API authentication, structured outputs, environment variables, and debugging browser behavior.

Billing activation, version-folder confusion, and setup errors consumed valuable time, but each problem became part of the project’s quality-control process.

What I learned I learned that strong AI governance can be translated into practical product behavior. I also learned that human judgment remains essential: the model can organize evidence, expose gaps, and calibrate language, but people must still decide what evidence is sufficient and what risks matter.

BoundaryLens demonstrates how human expertise and AI capability can work together without surrendering oversight.

Built With

• chatgpt-5.6-family