Tell AI to Define Its Own Terms from First Principles for Better Outputs and Auditable Reasoning

A Reddit user on r/ClaudeAI reports that instead of writing longer, more detailed prompts, they now add a single line: use Aristotelian first principles reasoning. before you proceed, break every undefined term down to its atomic meaning. This approach has produced markedly different and better outputs.

How It Works

When asked for "a world-class website," the AI used to produce generic, average results. With the first-principles instruction, it stops to define what "world-class" means — speed, visual hierarchy, accessibility, conversion patterns, trust signals — derives each component, and builds from there. The pattern holds across different tasks: vague adjectives that previously generated generic outputs now produce specific ones because the model reasons from component truths instead of pattern-matching to statistically common training data.

Key Benefit: Traceable Reasoning

The unexpected advantage is debuggability. When instructed to reason from first principles, the model builds a chain of reasoning. For example:

• "production-grade code means no silent failures"
• "no silent failures means every external call needs explicit error handling"
• "every API call needs a try/catch with a typed error response"

Each conclusion is valid only if the axioms above it are valid. When something goes wrong, you don't rewrite the prompt — you find the broken axiom. If axiom 6 is wrong, everything downstream becomes suspect. This creates a directed graph where every node has traceable parents.

In contrast, a normal long prompt makes a dozen decisions that exist nowhere, unreachable and unauditable. You either accept the output or start over.

Prompt Template

The user has shared a prompt template on GitHub: github.com/ndpvt-web/prompt-improver. They note that while "define your terms from first principles before proceeding" has been more reliable than adding paragraphs of constraints, edge cases remain — it's unclear if the technique holds equally across every model.

Who It's For

Developers using AI coding agents who want more precise outputs and the ability to audit and debug the AI's reasoning process.