Best Practices for Spec-Driven Development

Category: Best Practices Last updated: November 20, 2025

Overview

Effective Spec-Driven Development (SDD) requires more than just writing specifications before code—it demands disciplined practices that keep specifications human-reviewable, meaningfully decomposed, and continuously aligned with implementation. These best practices ensure teams maintain the value of specification-first development without overwhelming context windows or creating documentation that no one actually validates.

The practices outlined here focus on real-world effectiveness, particularly when working with AI coding agents that can quickly generate extensive specifications but require human oversight to maintain intentionality and clarity.

This is a curated summary from practical implementation guides. Read about managing context length in SDD →

Core Principles

Prioritize Human Reviewability

The fundamental test: Are the artifacts human reviewable? If you find yourself skimming specification changes while thinking "the AI probably got it right," the feature is too large. Specifications must maintain their intended clarity and intentionality rather than becoming auto-generated documentation no one actually validates.

Human review ensures specifications capture genuine design intent. When specifications become too lengthy to review effectively, they lose their value as sources of truth and become liability rather than asset.

Start with Minimal Specifications

Begin by fully specifying features concisely—avoid long chains of "and" statements that balloon complexity. Large monolithic specifications force AI agents into extensive clarification phases and exhaust context windows unnecessarily.

Minimal doesn't mean incomplete. It means capturing exactly what's needed for the current feature without prematurely specifying future requirements or edge cases that aren't immediately relevant.

Decompose Meaningfully

Break oversized features into valuable, independently-deliverable slices rather than attempting comprehensive specifications upfront. Apply frameworks like INVEST (Independent, Negotiable, Valuable, Estimable, Small, Testable) to validate story quality, and use MoSCoW prioritization (Must have, Should have, Could have, Won't have) to focus on must-haves first.

Meaningful decomposition creates features that provide value on their own, rather than arbitrary technical splits that deliver no user benefit until fully assembled.

Context Management Strategies

Address the "Lost in the Middle" Problem

While specifications help engineer context to avoid recency/primacy bias, oversized specs themselves suffer from these same issues. Forgotten clarifications and requirement drift can emerge during development when the specification document itself becomes too lengthy.

Keep specifications focused enough that the entire specification remains accessible to the AI agent or human developer throughout implementation. This prevents the specification from becoming part of the problem it's meant to solve.

Leverage Subagents Strategically

Delegate research and validation tasks to subagents with isolated contexts to reduce overall burden and enable parallelization—but only after right-sizing the feature itself. Subagents are a capability multiplier, not a substitute for good feature decomposition.

Tool Selection Matters Less Than Thinking

Switching tools or debating markdown efficiency won't solve fundamental decomposition problems. Structured thinking about feature sizing precedes any tooling optimization. Choose tools that support your workflow, but recognize that no tool compensates for poorly decomposed features.

Maintaining Specification Quality

Specification as Source of Truth

Maintain specifications as the authoritative reference for system behavior. This creates shared understanding across teams and provides a validation anchor for implementations. When specifications drift from implementation, they become unreliable—defeating the entire purpose of spec-driven development.

Systems Thinking for Cross-Feature Analysis

Features interact in non-linear ways. Apply systems thinking to detect cross-feature conflicts, feedback loops, and cascading effects before implementation. Understanding how features interact prevents emergent problems that appear only in production.

For example, adding an inventory alert feature to an e-commerce system might seem straightforward until systems analysis reveals potential notification storms during peak traffic when combined with existing features.

Continuous Validation

Specifications provide value when continuously validated against implementation. Automated checks ensure alignment between specification and code, catching drift early when it's cheapest to fix. Validation isn't a one-time gate—it's an ongoing practice throughout development.

Working Effectively with AI Agents

Specifications Prevent "Vibe Coding"

AI coding agents can quickly generate implementations, but without clear specifications, those implementations may drift from original intentions. Specifications provide design boundaries that keep agents aligned with intended outcomes rather than optimizing for speed alone.

Human Review and Intentionality

AI agents excel at translating clear specifications into code, but human judgment determines whether specifications capture the right intent. Maintain human oversight of specification quality—the human role shifts from writing code to ensuring specifications reflect genuine design decisions.

Parallel Development with Clear Specifications

Well-defined specifications enable parallel work streams. Different teams or agents can work simultaneously on frontend, backend, and integration when specifications clearly define interfaces and behavior. This parallelization accelerates delivery without sacrificing coordination.

Common Anti-Patterns to Avoid

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