About Spec Coding
Spec Coding helps engineering and product teams make decisions once — in writing — before coding begins. The result: fewer design debates during code review, QA that tests against concrete criteria, and releases that ship what was actually specified.
Who runs this site
Spec Coding grew out of Daniel's frustration with teams that shipped fast but reworked often. The site exists to make the spec-first approach practical and accessible — not theoretical. Every guide is written against real delivery scenarios, not textbook examples.
What we publish
Our content covers the full spec-first delivery lifecycle:
- Foundational guides on what spec-first development is, how it compares to agile, and how to adopt it in an existing team.
- API contract content covering OpenAPI-to-CI pipelines, versioning strategies, error handling patterns, and contract testing plans.
- Acceptance criteria and edge case examples for writing testable specs that QA can execute without interviewing the author.
- High-failure-impact topics including idempotency, race conditions, rollout/rollback strategies, and billing postmortems from when these weren't specified.
- Reusable templates such as downloadable spec templates, PRD-to-spec guides, and scoring tools for engineering review.
Why we emphasize spec-first
Many teams do not struggle because they cannot write code. They struggle because the critical decisions are still implicit when coding begins. A vague requirement becomes an API mismatch, a missing rollback plan, an unclear QA case, or an AI-generated change that quietly expands scope.
Spec-first work moves those decisions to the cheapest moment: before implementation. The spec is not paperwork for its own sake. It is the shared contract that lets product, engineering, QA, and AI coding tools operate against the same visible behavior.
Content method
- Start from failure modes. We choose topics by looking at where real delivery work breaks: ambiguous acceptance criteria, missing idempotency, unsafe migrations, unclear permissions, and rollout risk.
- Keep reusable structure. Guides include headings, checklists, markdown blocks, and review questions that readers can move into their own workflow.
- Separate principles from process. Principles explain why a practice matters; process sections show how to use it in planning, review, testing, and release.
- Avoid vague AI claims. AI can speed up implementation, but only when the input has boundaries, testable outcomes, and risk that reviewers can trace.
Who this is for
Spec Coding is for people who turn ideas into reliable shipped software: independent developers, startup teams, product managers, backend engineers, QA leads, engineering managers, and teams bringing AI coding tools into everyday delivery.
The material is most useful when your team keeps hitting the same friction: requirements that were discussed but not written down, API behavior that differs between consumers, tests that arrive after implementation, or generated code that goes beyond the intended scope.
Editorial standards
Every article on Spec Coding is written to be usable in a real engineering task — not to fill a word count. Our editorial principles:
- We favor concrete over abstract: real delivery scenarios, named failure modes, and testable acceptance criteria rather than vague advice.
- If a section does not help a reader make a decision or avoid a mistake, it is cut.
- Outdated guidance is revised when workflows, tools, or platform expectations change. Update dates are visible on every article.
- Factual mistakes reported by readers are investigated and corrected within five business days.
- The full publishing and review workflow is documented in our Editorial Policy.
How we judge whether a page is useful
Before publishing or expanding a page, we ask whether a reader can take a concrete next step: copy a checklist, choose a template, identify a missing risk, improve a prompt, or start a more focused review. If the page only repeats a definition without helping that next step, it is revised or merged into a stronger resource.
This is especially important for AI-assisted development content. The goal is not to claim that AI writes better code. The goal is to show how clear specs, non-goals, contracts, tests, and review evidence make AI output easier to control.
What we improve during content reviews
Content reviews focus on the parts that make a page useful in practice: concrete examples, clear reader intent, realistic failure modes, internal links to the next step, and enough context for a reader to apply the advice without a private explanation. A thin page is not fixed by adding slogans; it is fixed by adding a decision rule, a scenario, a checklist, or a stronger artifact the reader can reuse.
For bilingual pages, we also check whether the Chinese version preserves the same operational value as the English version. Translation should not remove the examples, caveats, or workflow details that make the original useful.
Advertising and independence
Spec Coding may display advertising (including Google AdSense) to support ongoing content maintenance and hosting costs. Our editorial positions — which templates we recommend, which patterns we endorse, which tools we describe — are not influenced by advertiser relationships. We do not sell paid placements inside guides or rankings inside comparison articles.
If a correction, update, or editorial decision is ever influenced by external commercial pressure, we will disclose it explicitly on the relevant page.
Privacy and data
We do not collect personal data beyond what standard hosting and analytics tools capture. See our Privacy Policy for full details on cookies, analytics, and advertising data. If you have a data-related question or request, email us directly.
Contact and corrections
For factual corrections, partnership inquiries, template feedback, or technical issues, reach us at [email protected] or use the contact page. We read every message and respond within three business days.