ChatGPT Prompts for Markdown Design Documents

Copy practical ChatGPT and Claude prompts for Markdown design documents, API specifications, detailed designs, and test plans, then prepare them for PDF.

Specify the document structure, not only the topic

Asking ChatGPT or Claude to “write a design document” often produces generic prose, excessive headings, wide tables, and confident guesses. A useful prompt defines the audience, confirmed inputs, excluded scope, treatment of unknowns, heading hierarchy, table limits, and output format.

AI is valuable for drafting and organizing information, but it can invent requirements, values, or references. A responsible engineer must verify facts, security decisions, names, constraints, and confidential information before the document is approved.

Prompt for a high-level design document

Act as a business-system design writer. Create a professional Markdown high-level design from the information below.

Goal: [system goal]
Audience: product managers, engineers, and operations
Confirmed requirements: [facts]
Out of scope: [excluded work]

Rules:
- Use one H1 for the document title
- Use H2 for major sections and H3 for supporting subsections
- Do not use H4 or deeper headings
- Do not write conversational introductions
- Never invent missing details; label them “To confirm”
- Limit tables to four columns and move long explanations below them
- Return Markdown only

Structure:
# System High-Level Design
## Document information
## Overview
## Background and goals
## Scope
## System architecture
## Feature list
## Data overview
## External interfaces
## Non-functional requirements
## Security
## Operations and monitoring
## Constraints
## Open questions

Requiring “To confirm” keeps uncertain content visible instead of allowing guesses to look like approved specifications. Add owners and due dates to those questions during review.

Prompt for detailed design

Create a Markdown detailed design from the approved requirements and high-level design.

For every feature include:
- Feature ID and name
- Inputs, processing, and outputs
- Happy path and failure paths
- Validation and authorization
- Logging and monitoring
- Related requirement IDs

Do not invent implementation code. Put uncertainty in an Open questions section. Use H2 for each feature and H3 for its design topics.

Supply existing naming rules, error-code conventions, and logging policy when they are safe to share. Never include production credentials or private tokens.

Prompt for an API specification

Turn the following API requirements into a reviewable Markdown specification.

For each API document:
- Endpoint and HTTP method
- Authentication and authorization
- Path, query, header, and body parameters
- Successful response example
- Error responses and their conditions
- Idempotency, timeout, and rate limits
- Audit logging

Put JSON examples in fenced code blocks. Use tables only for concise parameter comparisons. Mark unknown types, limits, and required fields as “To confirm.”

Keep representative JSON in the document and store a complete schema separately when it would make the PDF difficult to read.

Prompt for a test specification

Produce Markdown test scenarios from the requirements and design.

Separate happy paths, boundaries, failures, permissions, and concurrency. Give each case an ID, prerequisites, action, expected result, and related requirement ID. Do not invent expected results. Move uncertainty into a separate section. Keep tables concise and place long procedures below each case.

A large number of AI-generated cases does not prove coverage. Review traceability, risk, feasibility, and duplicated cases.

Review the generated Markdown

Check whether the AI added unrequested features or presented uncertain details as facts. Confirm one H1 and a consistent H2/H3 hierarchy. Reduce wide tables, long URLs, logs, and JSON. Remove duplicated introductions and conclusions. Verify citations, requirement IDs, security decisions, personal data, and confidential details.

Ask for targeted revisions such as “move unknown values to Open questions” rather than “make it better.” Small instructions produce changes that are easier to review.

Turn the draft into a PDF

Save the answer as .md and load it into a converter with print preview. Review the cover, TOC, margins, numbering, code, tables, images, and page breaks. A formal specification benefits from consistent document metadata; a short handout usually needs fewer front-matter elements.

If a heading, table, or diagram splits poorly, adjust print CSS or the converter's page-break settings. Size images appropriately in the source and use CSS for alignment when needed. Reopen the saved PDF and check the page count, TOC, glyphs, links, and final page.

FAQ

Can an AI response be submitted without review?

No. Treat it as a draft. The model cannot approve requirements or guarantee factual accuracy, security, compliance, or organizational conventions.

Do these prompts work with Claude?

Yes. Results vary by model, so keep the hierarchy, no-invention rule, and Markdown-only output explicit.

Should a long specification be generated in one request?

Create and approve the outline first, then draft chapters from confirmed source material. Finish by standardizing terminology, tone, and requirement IDs across chapters.

Check the finished PDF

Turn an AI-generated design document into PDF