A technical design doc describes how you plan to build something before you build it. Its purpose is not paperwork. It is to find the expensive mistakes while they are still cheap: the wrong data model, a missed requirement, a dependency on a team that cannot deliver in time.

In 2026 there is a second benefit. A clear design doc is excellent context for AI coding agents, which produce far better results when the goals, constraints and chosen approach are written down.

When to write one

Write a design doc when a change:

  • Takes more than a week or two of work.
  • Changes a data model, a public API or a security boundary.
  • Involves more than one team.
  • Is hard to reverse.

For small, reversible changes, a good pull request description is enough.

What makes a design doc useful

  • It states the problem before the solution. Reviewers can then judge whether the solution fits.
  • It shows alternatives. Explaining why you rejected options builds confidence in the choice.
  • It is short. Most good design docs are 2–6 pages. Link to details rather than including them.
  • It names risks and open questions honestly.

A template you can copy

markdown
# Title

Author · Reviewers · Status (Draft / In review / Approved) · Date

## Context
What is the situation today, and why does it need to change?

## Goals and non-goals
- Goals: what success looks like, ideally measurable.
- Non-goals: what this work deliberately does not cover.

## Proposed design
The approach, with a diagram. Data model, APIs, key flows.

## Alternatives considered
Each option, its trade-offs and why it was not chosen.

## Security, privacy and reliability
Threats, data handling, failure modes, monitoring.

## Rollout and migration
Feature flags, data migration, backward compatibility, rollback plan.

## Open questions
What still needs a decision, and who decides.

Writing tips

  • Lead with a one-paragraph summary so busy reviewers get the point quickly.
  • Use diagrams for flows and architecture; a simple box-and-arrow sketch is enough.
  • Be specific about numbers: expected traffic, data size, latency targets.
  • Write non-goals. They prevent scope creep and misunderstandings.

Running the review

  • Share the doc early, while changes are still easy.
  • Ask reviewers specific questions ("Is the migration plan safe?") rather than "any thoughts?".
  • Record decisions in the doc so it stays the source of truth.

Keep it useful after approval

Update the status when the design changes during implementation. Link the doc from the code and pull requests, and add it to the project's context files so future humans and AI agents understand why the system looks the way it does.

Key takeaways

  • Design docs catch expensive mistakes early.
  • State the problem, goals, non-goals and alternatives.
  • Keep it short, specific and honest about risks.
  • A good design doc is also strong context for AI coding agents.