Write the Spec, Not the Prompt

2026-07-26 · Source: AI on Medium · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

AI coding agents accelerate implementation but shift the development bottleneck from typing to upfront decision-making, increasing cognitive load and leading to subtly incorrect code if prompts are vague. This article advocates for spec-driven development, where clear, written specifications guide AI agents, preventing errors and reducing expensive iterative corrections. The author illustrates this with examples like ensuring idempotent authentication initialization and defining project scope early, such as "this version does not talk to a bank." Writing test cases using a Given/When/Then structure is presented as an effective way to create unambiguous specs that agents can directly use. Existing tools like Architecture Decision Records (ADRs), RFCs, and productized AI workflows like Kiro's spec mode and GitHub's Spec Kit are highlighted as newly relevant for ensuring reliability and accuracy in AI-assisted coding.

Key takeaway

For software engineers working with AI coding agents, you should shift your focus from refining prompts to developing clear, concise specifications. Instead of iterative trial-and-error, define the problem, constraints, and expected behavior upfront, as if onboarding a new hire. This approach will significantly reduce costly corrections, improve code reliability, and ultimately make your AI-assisted development workflow faster and more accurate.

Key insights

AI coding agents necessitate spec-driven development to resolve ambiguity upfront, ensuring reliable and efficient code generation.

Principles

• Vague prompts yield subtly wrong, expensive code.
• Explicitly document constraints for AI agents.
• Upfront specification enhances agent speed and accuracy.

Method

Develop a spec covering intent, constraints, key behaviors ("Given/When/Then"), and a human review checkpoint before AI code generation.

In practice

• Frame behavior specs using "Given/When/Then".
• Document architectural decisions with ADRs.
• Define project non-goals to prevent scope creep.

Topics

• Spec-Driven Development
• AI Coding Agents
• Software Engineering
• Test-Driven Development
• Architecture Decision Records
• Given-When-Then

Best for: Software Engineer, AI Engineer, Director of AI/ML

Related on AIssential

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• I Let an AI Agent Build a Feature End-to-End. Here’s What Broke. — AI agents fail like junior engineers, especially at edge cases, scale, and security, requiring human oversight.
• Building With AI Agents Without Losing Your Architecture — Spec-driven development, using living contracts and deterministic tests, guides AI coding agents to maintain architectural integrity.
• SE Radio 710: Marc Brooker on Spec-Driven AI Dev — Explicit specifications are the scalable foundation for AI-assisted software development, shifting focus to design and validation.
• Fragments: April 29 — Agentic engineering shifts focus from building fast to verifying fast, emphasizing automated checks and clear definitions.
• Why AI Coding Agents Still Need Clear Specs — Minimal specification for AI coding agents defers costs, making detailed, validated specs crucial for efficiency.

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI on Medium.