Why this is hard to get right
Priya is a platform engineer at a mid-size SaaS company. Her team just finished building an internal CLI tool — stackctl — that lets backend developers provision staging environments without touching Terraform directly. The tool has been live for two weeks, and the Slack channel is already filling up with "how do I use the --region flag?" questions.
She knows every flag by heart. But sitting down to write the docs? That's a different story.
Her first attempt is a Google Doc titled "stackctl overview" that gets three paragraphs in before she starts copying terminal output and pasting it without context. The document doesn't explain prerequisites. It doesn't show what a successful run looks like. It lists flags but not when to use them. She shares it in Slack anyway, and the questions keep coming.
She tries asking ChatGPT to "write docs for my CLI tool." The output is a generic, cheerful README template that could apply to any tool ever built. It has no knowledge of her commands, her audience, or the AWS-specific context that makes stackctl different from anything else.
The real problem is that Priya's prompt gave the AI nothing specific to work with.
Without knowing the tool's name, the command structure, the reader's experience level, or the expected format, the AI defaults to the most average documentation it can imagine. It produces something that looks like documentation but doesn't actually document anything.
What Priya needs is a prompt that front-loads all of that context: the tool name, the three primary commands, the fact that her readers are backend engineers who understand Kubernetes but have never used stackctl, and the preference for Markdown tables over prose lists.
When she builds that prompt — with AskSmarter.ai walking her through each dimension — the output is a draft she can ship with one editing pass. The questions stop coming in Slack. New engineers onboard themselves. And Priya gets her afternoon back.
Common mistakes to avoid
Omitting the Target Reader's Experience Level
Saying 'write docs for developers' forces the AI to guess what developers already know. A senior SRE and a junior frontend engineer need completely different explanations. State the reader's specific background explicitly to calibrate depth and jargon.
Listing Commands Without Showing Expected Output
Documenting what a command accepts without showing what it returns leaves users guessing whether it worked. Always prompt the AI to include a sample output or success confirmation for each key command.
Requesting 'All Commands' Without Naming Them
When you don't name specific commands, the AI invents plausible ones or produces generic placeholders. List the actual command names and flags you want documented, even if briefly, so the output reflects your real tool.
Skipping the Error and Troubleshooting Section
First-time users will hit errors. Documentation that only shows the happy path forces everyone into Slack or Stack Overflow. Explicitly ask for a common errors section with cause-and-fix pairs to reduce support burden.
Not Specifying the Output Format
Without a format instruction, AI documentation output varies wildly — sometimes prose paragraphs, sometimes bullet lists, sometimes inconsistent heading levels. Specify Markdown, table structure, and heading hierarchy to get docs that fit your existing style.
The transformation
Write documentation for my CLI tool that deploys apps to the cloud. Include the commands and flags.
**Act as a senior technical writer with expertise in developer tooling documentation.** Write user documentation for a CLI tool called `deployr` used by mid-level backend engineers to deploy Node.js applications to AWS. **Structure the documentation as follows:** 1. Overview (2-3 sentences describing what the tool does and who it is for) 2. Installation section (with a code block showing npm install and verification step) 3. Core commands reference table (command | flags | description | example) 4. 3 real-world usage examples with annotated code blocks 5. Common errors section with cause and fix for each **Constraints:** - Use plain Markdown with H2/H3 headings - Keep descriptions under 20 words per command - Assume readers know Node.js but are new to `deployr` - Do not include internal implementation details
Why this works
Specificity
Naming the tool, the commands, and the deployment target (e.g., AWS) gives the AI real anchors. It stops producing generic filler and starts producing content that actually describes your tool's behavior and context.
Audience Definition
Stating the reader's experience level and background allows the AI to calibrate every word choice. It skips explanations the reader already knows and adds depth where they're most likely to get stuck.
Structure
An explicit section-by-section outline prevents the most common documentation failure: the undifferentiated wall of text. The AI follows the structure like a template, producing a scannable, navigable document from the first pass.
Constraints
Hard rules like 'under 20 words per command description' or 'no internal implementation details' act as an editorial policy. They keep the AI from over-explaining or drifting into irrelevant territory.
Deliverable Clarity
Asking for specific artifacts — a reference table, annotated examples, an error guide — mirrors how real documentation is organized. The AI produces each as a discrete, usable component rather than one blended block.
The framework behind the prompt
Good CLI documentation follows a well-established information architecture called the Diátaxis framework, developed by Daniele Procida. Diátaxis divides technical documentation into four distinct types: tutorials (learning-oriented), how-to guides (task-oriented), reference material (information-oriented), and explanations (understanding-oriented). Most CLI docs fail because they try to do all four in a single undifferentiated page.
When you prompt AI to generate CLI documentation, you're essentially asking it to select and apply one or more of these modes. Without explicit instruction, AI defaults to a hybrid that satisfies none of them well — too shallow to teach, too narrative for quick reference, too sparse to explain design decisions.
The PARA principle (Purpose, Audience, Reference, and Actions) offers a practical framework for CLI prompts specifically. Each section of your documentation should answer one of these four questions: Why does this tool exist? Who is it for? What does each command do? How do I accomplish a specific task?
Structuring your AI prompt around these four questions — even implicitly — produces documentation that engineers can navigate rather than read linearly. Combined with explicit format constraints (Markdown tables, code blocks, H2/H3 hierarchy), this framework produces first-draft documentation that requires editing, not rewriting.
Prompt variations
Act as a senior technical writer specializing in open-source developer tools.
Write a GitHub README for a CLI tool called snapenv that creates and restores development environment snapshots.
Structure:
- Badges line (build, version, license)
- One-sentence description and use case
- Installation (with Homebrew and npm options)
- Quick start (3 commands to get running in under 2 minutes)
- Full command reference table
- Contributing section (one paragraph)
Constraints:
- Target audience: open-source contributors who use macOS or Linux
- Use GitHub-flavored Markdown
- Keep the tone friendly and welcoming
- Total length: 400-600 words
Act as a technical writer embedded in a platform engineering team.
Write internal documentation for a CLI tool called vaultctl that manages secrets rotation in our Kubernetes clusters.
Structure:
- Purpose and scope (what this tool does and does NOT do)
- Prerequisites (access permissions, required env vars)
- Command reference with flags and example usage
- Runbook-style section: 'Rotating a production secret step by step'
- Security warnings and restricted flags section
Constraints:
- Audience: DevOps engineers with Kubernetes experience
- Assume readers have cluster access already configured
- Flag any commands that require elevated permissions with a warning callout
- Use Confluence-compatible Markdown
When to use this prompt
Developer Advocates
Quickly produce getting-started guides for open-source CLI tools ahead of a launch or conference demo, without spending a full sprint on documentation.
Platform Engineering Teams
Document internal deployment or infrastructure CLIs so that other engineering teams can onboard themselves without scheduling walkthroughs.
Product Managers Owning Developer Tools
Draft a first version of CLI docs to hand off to engineering for review, reducing the time the team spends writing from scratch.
Backend Engineers Shipping Side Projects
Generate polished README-style documentation for personal or freelance CLI tools that need to look professional for GitHub or NPM publishing.
Technical Writers on Tight Deadlines
Use a structured prompt to produce a complete documentation draft in one pass, then refine rather than starting with a blank page.
Pro tips
- 1
Specify the reader's experience level explicitly — saying 'assumes familiarity with Docker but not with this tool' gives the AI a precise editorial baseline that affects every word choice.
- 2
Name the exact commands and flags you want covered rather than saying 'all commands.' The AI will produce deeper, more accurate content for five named commands than shallow coverage of a vague full set.
- 3
Include a target length or section word count if your documentation lives inside a larger system. Saying 'keep each command description under 20 words' prevents bloat that breaks style guides.
- 4
Request a specific Markdown structure (H2, H3, code blocks, tables) if your docs platform or README renderer has formatting requirements. This saves a full editing pass after generation.
A command reference table is the most-visited section of any CLI documentation. Engineers return to it every time they forget a flag. Getting it right matters.
The four columns every CLI table needs:
| Column | What to Include | |---|---| | Command | Full command string including subcommand | | Flags | Required and optional flags with types | | Description | What it does in under 20 words | | Example | A copy-paste-ready terminal snippet |
Prompt instruction to add: Ask the AI to bold required flags and italicize optional ones. This single formatting convention reduces 'why isn't this working?' questions by 40% among new users.
Ordering matters: List commands in order of how frequently they'll be used, not alphabetically. Alphabetical ordering optimizes for completeness; frequency ordering optimizes for new users, who are your highest-cost support segment.
Include the output column: Add a fifth column — 'Expected output' — that shows the first line of a successful response. Engineers use this to confirm the command ran correctly, especially in scripted environments where they won't see the full terminal output.
Annotated examples are the highest-value section of CLI documentation, and the easiest to get wrong. A bare code block shows what to type. An annotated example explains why.
Ask the AI to produce examples in this format:
# Deploy the staging environment to us-east-1
# Requires: AWS credentials configured via `deployr auth`
deployctl deploy --env staging --region us-east-1 --confirm
# Output: 'Deployment queued: job-id-4821. ETA 90s.'
Three types of examples to request in every prompt:
- Happy path — the most common use case with expected output shown
- With optional flags — showing how flags change behavior, not just syntax
- Error scenario — a deliberate mistake with the corrected command below it
Why error examples matter: Documentation that only shows success teaches users what to type. Documentation that shows errors teaches users what the tool is doing and how to reason about it. Engineers who understand error messages file fewer support tickets.
Prompt addition: Add this line to your prompt: 'For each example, include a one-line comment above explaining when to use this command, and a one-line comment below showing the first line of expected output.'
CLI tools change. Commands get deprecated, flags get renamed, and new subcommands appear. Keeping documentation current is a maintenance burden that most teams underestimate until it causes an incident.
Use AI to produce a versioned documentation stub:
Add this section to your prompt:
'Include a version compatibility table at the top showing which commands are available in v1.x vs v2.x. Mark any deprecated flags with a warning callout.'
Changelog-driven documentation updates: When you ship a new version, paste your git changelog into the prompt with the instruction: 'Update the existing documentation sections affected by these changes and flag any deprecated commands with a deprecation notice and migration path.'
The migration guide pattern: For major version changes, ask the AI to produce a dedicated migration section:
'Write a migration guide for users upgrading from v1 to v2. For each changed command, show the v1 syntax, the v2 syntax, and the reason for the change in one sentence.'
This pattern reduces migration-related support volume because users can self-serve the upgrade without reading a diff.
When not to use this prompt
This prompt pattern is not the right tool when your CLI tool has no stable API yet. Generating documentation for commands that are still changing creates a maintenance debt that compounds with every release.
It's also a poor fit for documenting highly security-sensitive internal tools where the documentation itself — listing flags, endpoints, or environment variable names — could expose attack surface. In those cases, generate a documentation outline only and fill in sensitive specifics manually.
Finally, if your tool requires deep domain knowledge to use (e.g., a bioinformatics pipeline CLI), AI-generated examples may contain plausible but incorrect domain-specific usage. Always have a domain expert review examples before publishing.
Troubleshooting
The AI invents commands and flags that don't exist in my tool
Paste your actual command signatures directly into the prompt before asking for documentation. Even a rough list works: '--deploy [env], --rollback [job-id], --status [env]'. Tell the AI explicitly: 'Document only the commands listed below. Do not infer or add any additional commands.' This hard constraint stops fabrication.
The output reads like a generic README template, not documentation for my specific tool
Add three specifics to your prompt: the tool's actual name, one sentence about what problem it solves that no other tool solves, and a named example of a real workflow. Replace 'my CLI tool' with 'deployctl, a CLI that deploys Node.js apps to AWS ECS without writing task definitions.' Generic prompts produce generic docs.
The documentation is technically accurate but impossible for new users to follow
Add a prerequisites section instruction and an explicit beginner constraint: 'Assume the reader has never run this tool before. Include a prerequisites section listing required accounts, permissions, and environment variables. Add a Verification step after installation showing what a successful setup looks like.' Accuracy without onboarding context is the most common documentation failure mode.
How to measure success
A successful AI output from this prompt will include all five requested sections without merging them. The command reference will appear as a Markdown table, not a bullet list. Each command description will stay under 20 words. Code blocks will use proper syntax highlighting tags. Examples will include both a command and its expected output.
The most important quality signal: give the output to someone who has never used your tool and ask them to complete the most common task. If they succeed without asking a question, the documentation is working. If they get stuck, identify which section failed and tighten that specific prompt instruction for your next iteration.
Now try it on something of your own
Reading about the framework is one thing. Watching it sharpen your own prompt is another — takes 90 seconds, no signup.
CLI tool documentation for backend engineers
Try one of these
Frequently asked questions
Yes. Describe the commands you've already built and note which are planned. Ask the AI to mark planned features with a 'Coming soon' label. Writing docs early also surfaces gaps in your command design before users find them.
Paste your real command signatures directly into the prompt. Even a rough list like '--deploy [env] --region [us-east-1]' gives the AI accurate syntax to document. The more literal input you provide, the less the AI will fabricate.
Break it into sections. Use one prompt per major command group (e.g., 'auth commands', 'deploy commands', 'config commands'). This keeps each output focused and high quality rather than producing a shallow overview of everything.
Add a language instruction at the top: 'Write this documentation in Spanish for developers in Latin America.' Also specify whether to use formal or informal address (usted vs. tú), as this significantly affects technical tone in many languages.
Yes. Replace the Markdown constraint with your target format: 'Use reStructuredText for Sphinx,' 'Format as an HTML page with semantic headings,' or 'Output as a JSON schema for our docs API.' The structure stays the same; only the rendering format changes.