Technical Documentation Explained: Creating Documentation That Actually Gets Used

In 2009, Stripe launched with extraordinary API documentation. Every endpoint included working code examples in seven programming languages. Error messages were clear and actionable. Conceptual guides explained authentication, idempotency, and webhooks with diagrams and practical examples. Developers called it "the best API documentation they'd ever used."

The result: Stripe grew faster than competitors despite entering a crowded payments market. Technical founders chose Stripe not because it had superior technology, but because they could integrate it in an afternoon instead of spending weeks deciphering obtuse documentation. Stripe's documentation became product differentiator and competitive advantage.

Contrast this with most technical documentation: outdated, incomplete, organized from the developer's perspective rather than user's needs, filled with jargon and missing practical examples. Users encounter errors that documentation doesn't mention. Code examples don't work. Basic questions go unanswered. Frustration drives users away—not because the product is bad, but because nobody can figure out how to use it.

This analysis examines what makes technical documentation effective: the types, structures, and practices that create documentation users actually use rather than ignore; how to synchronize docs with code; common failures and how to avoid them; and why documentation is often an organization's most valuable and most neglected asset.

What Is Technical Documentation and Why It Matters

Defining Technical Documentation

Technical documentation: Structured information explaining how technical systems work, how to use them, and how to solve problems with them.

Includes:

  • API documentation: Endpoint references, authentication, request/response formats.
  • User guides: Feature explanations, tutorials, how-to guides.
  • Architecture documentation: System design, component interactions, technical decisions.
  • Runbooks: Operational procedures, troubleshooting, incident response.
  • Code comments and README files: Inline explanations, project setup, development guides.

Distinguishing characteristics:

  • Technical audience: Developers, engineers, technical users (though "technical" doesn't mean "incomprehensible"—clarity still essential).
  • Practical focus: Helps users accomplish tasks, not literary or entertaining.
  • Accuracy requirement: Minor errors destroy credibility. Documentation must match implementation exactly.
  • Maintainability: Must evolve with product through many releases and contributors.

Why Documentation Is Competitive Advantage

1. Reduces adoption friction: Users evaluate products by time-to-first-success. Clear documentation means users get working integrations in hours instead of days. Faster time-to-value increases conversion and reduces churn.

2. Scales support: Good documentation answers questions users would otherwise ask support. Shifts from linear scaling (one support engineer per X users) to sublinear (documentation serves unlimited users).

3. Enables self-service: Users prefer solving problems themselves at 3am versus waiting for support. Documentation provides 24/7 assistance.

4. Builds trust: Comprehensive, accurate documentation signals professional, reliable product. Sparse or wrong documentation signals neglect.

5. Reduces onboarding time: New team members (users' teams and your own) learn faster with clear documentation, reducing ramp time from weeks to days.

The Documentation Paradox

Reality: Despite benefits, documentation chronically neglected.

Common excuses:

  • "Code is self-documenting": It's not. Code shows what and how, rarely why or when.
  • "Documentation gets out of date": Only because not maintained. Solution is process, not skipping docs.
  • "Users don't read documentation": True when documentation is bad. Users read good documentation.
  • "We'll document later": Later never comes. Technical debt accumulates.

Root cause: Documentation feels like busywork—no immediate feedback, unclear impact, perceived as non-technical task. Engineering culture often undervalues it. This is mistake. Documentation is engineering work affecting user experience as much as UX design or performance optimization.

Types of Technical Documentation: The Diátaxis Framework

Diátaxis framework (Daniele Procida): Categorizes documentation by user need and purpose. Four types: Tutorials, How-To Guides, Reference, Explanation.

1. Tutorials: Learning-Oriented

Purpose: Teach through guided, step-by-step lessons. Get users from "never used this" to "basic working understanding."

Characteristics:

  • Learning by doing: Hands-on exercises, not just reading.
  • Safe environment: Simple, controlled examples avoiding complexity.
  • Successful outcome: User builds something working and meaningful.
  • Explanation: Teaches concepts and principles, not just mechanics.

Example: "Build Your First REST API in 30 Minutes" walking through authentication, creating endpoints, handling requests, returning JSON, testing—with explanations of REST principles, HTTP methods, status codes along the way.

When users need this: Onboarding, evaluating product, learning new concepts.

Common mistakes:

  • Too complex: Tutorials aren't production guides. Simple examples, not edge cases.
  • Unexplained magic: Don't just give commands to copy—explain what each step does and why.
  • Assuming knowledge: Define terms, link to prerequisites. Not everyone knows what "CORS" means.

2. How-To Guides: Task-Oriented

Purpose: Solve specific problems users encounter. "How do I...?"

Characteristics:

  • Problem-focused: Organized by what users want to accomplish, not by system structure.
  • Assumes knowledge: Users understand basics; guides focus on specific tasks.
  • Practical and direct: Minimal explanation—show solution, user adapts to their context.
  • Real-world scenarios: Common use cases users actually face.

Example: "How to Handle Rate Limiting," "How to Paginate Large Result Sets," "How to Retry Failed Requests."

When users need this: Implementing specific features, solving known problems, past initial learning.

Common mistakes:

  • Too conceptual: How-to guides should be recipes, not essays. Code and steps, minimal theory.
  • Unrealistic examples: Use real scenarios, not trivial toy cases.
  • Missing edge cases: Real implementations encounter errors, edge cases. Address them.

3. Reference: Information-Oriented

Purpose: Comprehensive, authoritative details about all functionality. "What are all the parameters for X?"

Characteristics:

  • Complete: Every endpoint, parameter, configuration option, error code. No gaps.
  • Structured and consistent: Uniform format—users know where to find information.
  • Technical and precise: Exact types, constraints, defaults. No ambiguity.
  • Searchable: Users don't read references linearly—they search and jump to specific items.

Example: API reference listing every endpoint with:

  • URL and HTTP method: POST /api/v1/users
  • Authentication requirements: OAuth 2.0, requires user:write scope
  • Parameters: name (string, required), email (string, required, must be valid email), role (enum: 'admin', 'user', default 'user')
  • Response: JSON schema with example, all possible fields documented
  • Errors: All HTTP status codes and error messages

When users need this: Building production systems, debugging, looking up specific details.

Common mistakes:

  • Incomplete: Missing parameters, undocumented error codes, vague types. Every detail must be specified.
  • Inconsistent: Different endpoints documented differently. Consistency enables pattern recognition.
  • No examples: Reference should include example requests and responses, not just schemas.

4. Explanation: Understanding-Oriented

Purpose: Provide context, rationale, and deeper understanding. "Why does this work this way?"

Characteristics:

  • Conceptual: Explains ideas, architectures, design decisions.
  • Background and context: History, tradeoffs, alternatives considered.
  • Clarifies relationships: How pieces fit together, dependencies, workflows.
  • No direct action: Not step-by-step instructions—deepens understanding.

Example: "Why We Use JWT for Authentication" explaining token-based auth vs. sessions, statelessness benefits, security considerations, expiration and refresh strategies.

When users need this: Understanding system deeply, making architectural decisions, debugging complex problems, evaluating whether product fits needs.

Common mistakes:

  • Too abstract: Vague generalities without connecting to specific product.
  • Mixing with how-to: Explanations aren't tutorials or guides. Separate concerns.
  • Assuming expertise: Explain clearly, even complex topics. Don't hide behind jargon.

Combining Types: Complete Documentation

Effective documentation includes all four types:

  • Tutorials get users started.
  • How-to guides solve specific tasks.
  • Reference provides complete details.
  • Explanations build deep understanding.

Users move between types depending on context—a new user starts with tutorials, then uses how-to guides and reference during implementation, and reads explanations when encountering complex problems.

Single-type documentation fails: Only tutorials can't support production use. Only reference overwhelms beginners. Only explanations don't show how to do anything.

API Documentation: Special Considerations

OpenAPI/Swagger: Spec-Driven Documentation

OpenAPI Specification (formerly Swagger): Machine-readable format for describing REST APIs.

Benefits:

  • Single source of truth: API definition in YAML/JSON drives both implementation and documentation.
  • Automatic documentation generation: Tools (Swagger UI, ReDoc, Redocly) generate interactive docs from spec.
  • Validation: Request/response validation against spec catches discrepancies between code and docs.
  • Code generation: Generate client libraries, server stubs, tests from spec.

Workflow:

  1. Write OpenAPI spec defining endpoints, parameters, responses.
  2. Implement API matching spec (or generate spec from code using tools like springdoc-openapi, FastAPI).
  3. Generate documentation automatically from spec.
  4. Keep spec updated as API changes—spec is authoritative.

Limitation: Generated docs are comprehensive reference but lack tutorials, how-tos, explanations. Still need hand-written content for learning and understanding.

Code Examples: Most Valuable Asset

Users want working code they can copy, modify, and run. Text descriptions alone insufficient.

Best practices:

  • Multiple languages: If API serves polyglot users, provide examples in popular languages (JavaScript, Python, Java, Go, Ruby).
  • Complete examples: Not just API call—include authentication, error handling, parsing responses.
  • Realistic scenarios: Use real-world use cases, not trivial "hello world."
  • Copy-pasteable: Users should be able to copy, add credentials, and run. No missing imports or undefined variables.
  • Tested: Code examples that don't work destroy trust. Automate testing—examples should be part of test suite.

Example structure:

import requests

# Authentication
api_key = "your_api_key_here"
headers = {"Authorization": f"Bearer {api_key}"}

# Create user
user_data = {
    "name": "Jane Doe",
    "email": "jane@example.com"
}

response = requests.post(
    "https://api.example.com/v1/users",
    json=user_data,
    headers=headers
)

# Handle response
if response.status_code == 201:
    user = response.json()
    print(f"User created: {user['id']}")
elif response.status_code == 400:
    errors = response.json()["errors"]
    print(f"Validation failed: {errors}")
else:
    print(f"Error: {response.status_code}")

This shows authentication, request structure, success handling, error handling—everything user needs.

Error Documentation: Often Missing, Always Needed

Users encounter errors constantly. Undocumented errors force trial-and-error debugging.

Document every error:

  • HTTP status codes: What each code means in your API context.
  • Error response format: JSON schema for errors. Example:
    {
  "error": {
    "code": "INVALID_EMAIL",
    "message": "Email address must be valid",
    "field": "email"
  }
}
  • Common causes: Why this error occurs, what user likely did wrong.
  • How to fix: Actionable steps to resolve.

Example: Error: 429 Too Many Requests

  • Cause: Exceeded rate limit of 100 requests per minute.
  • Resolution: Implement exponential backoff and retry after Retry-After header seconds.
  • Prevention: Cache responses, batch requests, increase rate limit (contact support for enterprise plan).

Keeping Documentation Synchronized with Code

The Core Problem: Documentation Drift

Reality: Code and documentation diverge over time. Feature added, doc not updated. Parameter renamed, doc shows old name. New error codes, doc lists obsolete ones.

Result: Users can't trust documentation. Every statement must be verified through experimentation. Documentation becomes liability, not asset.

Solution: Docs-as-Code

Principle: Treat documentation like code—versioned, reviewed, tested, deployed alongside implementation.

Practices:

1. Colocate docs with code: Store documentation in same repository as code. Markdown files alongside source files.

2. Update docs in same PR as code changes: Definition of done includes documentation. Code review includes doc review.

3. Automate verification: Generate reference docs from code (OpenAPI specs, docstrings, type annotations). Impossible for docs to drift when generated from implementation.

4. Test doc examples: Code snippets in docs should be executable tests. If example breaks, tests fail, forcing fix.

5. Version docs with product: Each release has corresponding documentation version. Users of older versions access old docs.

Example workflow:

  • Developer adds new API endpoint.
  • PR includes: implementation, OpenAPI spec update, how-to guide showing usage, test covering example code.
  • CI runs: unit tests, integration tests, documentation build (fails if spec invalid), example code tests (fails if snippets don't work).
  • Reviewer checks: code quality, test coverage, documentation accuracy and clarity.
  • Merge: documentation deploys automatically with code.

Automating Documentation Generation

Tools:

  • OpenAPI/Swagger: REST API documentation from OpenAPI spec.
  • GraphQL: Schema introspection generates documentation automatically.
  • Docstrings/Comments: Tools (JSDoc, Sphinx, Javadoc, GoDoc) generate reference docs from code comments.
  • Type annotations: TypeScript, Python type hints become documentation.

Benefits:

  • Accuracy guaranteed: Documentation reflects code because generated from code.
  • Low maintenance: Docs update automatically when code changes.
  • Developer-friendly: Writing docstrings feels like coding, not separate documentation task.

Limitations:

  • Reference only: Automated tools generate reference docs, not tutorials, how-tos, or explanations.
  • Quality depends on input: Generated docs only as good as docstrings. Lazy docstrings produce lazy docs.

Best practice: Automate reference, hand-write learning materials. Let tools handle boring, error-prone work (parameter lists, schemas). Invest human effort in tutorials and guides where creativity and user empathy matter.

Common Documentation Failures and How to Avoid Them

1. Organized by System Structure, Not User Tasks

Failure: Documentation mirrors internal architecture—organized by microservices, modules, classes.

Why it fails: Users don't care about your architecture. They care about accomplishing tasks.

Example of bad structure:

  • Auth Service
    • Token Management
    • Session Handling
  • User Service
    • User CRUD
    • Profile Management
  • Payment Service
    • Charge Processing
    • Refund Handling

User thinking: "I want to create a user and charge their credit card. Where do I look?"

Better structure:

  • Getting Started
  • User Management
    • Creating Users
    • Updating Profiles
  • Payments
    • Charging Customers
    • Handling Refunds
  • Authentication
    • API Keys
    • OAuth2

Organized by tasks users want to accomplish, not internal services.

Fix: Interview users. What are they trying to do? Organize docs around those goals.

2. Assuming Knowledge Users Don't Have

Failure: Documentation uses jargon, acronyms, assumes context readers lack.

Why it fails: New users get lost immediately. Documentation becomes impenetrable wall.

Example: "Configure your IdP to return SAML assertions including the NameID claim with emailAddress format. Ensure SPInitiated SSO is enabled and ACS URL matches your environment."

Translation for non-experts needed: "To set up single sign-on, configure your identity provider (the system your users log into, like Okta or Azure AD) to send their email addresses when they log in. You'll need to enable 'SP-Initiated SSO' (Service Provider Initiated Single Sign-On, meaning login starts from our app) and set the 'ACS URL' (Assertion Consumer Service URL—the web address where we receive login information) to [URL for your environment]."

Fix: Define terms on first use. Link to glossary. Provide examples. Test documentation on someone unfamiliar with your domain.

3. No Examples or Only Toy Examples

Failure: Documentation shows only trivial examples like "Hello World" without real-world complexity.

Why it fails: Users can't bridge gap from toy example to production use case. Real implementations encounter authentication, error handling, pagination, rate limiting—toy examples ignore these.

Bad example:

// Create a user
fetch('/api/users', {
  method: 'POST',
  body: JSON.stringify({name: 'John'})
});

What's missing: Authentication, headers, error handling, response parsing, validation.

Better example:

async function createUser(name, email) {
  try {
    const response = await fetch('https://api.example.com/v1/users', {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'Authorization': `Bearer ${API_KEY}`
      },
      body: JSON.stringify({ name, email })
    });

    if (!response.ok) {
      const error = await response.json();
      throw new Error(`API error: ${error.message}`);
    }

    const user = await response.json();
    return user;
  } catch (error) {
    console.error('Failed to create user:', error);
    throw error;
  }
}

Shows: Authentication, headers, error handling, response parsing—closer to production reality.

4. Outdated Information

Failure: Documentation describes old versions, deprecated features, removed functionality.

Why it fails: Users waste time implementing wrong approaches, encounter errors docs don't explain, lose trust in documentation.

Fix: Versioned documentation (users of v1 see v1 docs, v2 users see v2 docs). Automated checks for broken links, deprecated functions. Regular documentation audits—every 6 months, review and update.

5. No Clear Entry Point

Failure: Documentation is comprehensive but users can't find relevant information. No clear starting point, poor navigation, weak search.

Why it fails: Users give up if they can't find answers quickly. They'll use trial-and-error or ask support instead.

Fix:

  • Clear navigation: Logical hierarchy, visible in sidebar or menu.
  • Powerful search: Full-text search with autocomplete, keyword highlighting, relevance ranking.
  • Landing page: Directs users to appropriate starting point—new users to getting started, existing users to reference or guides.
  • Direct links: When users google "your-product authentication," they should land directly on authentication documentation, not homepage.

Writing for Clarity: Practical Techniques

1. Start with the User's Question

Every doc page should answer specific question: "How do I...?" "What is...?" "Why does...?"

Open with the answer: Don't bury the lead. First sentence should address the question.

Bad: "Authentication is a critical security component in modern web applications. There are many approaches including session-based, token-based, OAuth, and SAML..."

Good: "To authenticate API requests, include your API key in the Authorization header: Authorization: Bearer YOUR_API_KEY"

2. Use Active Voice

Active voice: Subject performs action. "The API returns a JSON response."

Passive voice: Action performed on subject. "A JSON response is returned by the API."

Passive voice is wordier, vaguer, less direct. Active voice clearer.

3. Short Sentences and Paragraphs

Technical writing isn't literature—don't try to impress with complexity.

Keep sentences under 20-25 words when possible. Break long paragraphs into shorter chunks. White space improves readability.

4. Use Formatting for Scannability

Users scan before reading. Help them find relevant information quickly:

  • Headings: Descriptive, hierarchical. Users should understand content from headings alone.
  • Lists: Bullet points for items, numbered lists for sequences.
  • Code blocks: Syntax highlighting, copy button.
  • Bold/Italics: Highlight key terms, warnings, important concepts.
  • Tables: Compare options, list parameters with types and descriptions.

5. Show, Don't Just Tell

Visuals clarify: Diagrams showing architecture, workflows, data flow. Screenshots showing UI steps. Flowcharts for decision trees.

Code examples are "showing"—demonstrating how rather than describing.

Analogies and metaphors help explain abstract concepts.

Measuring Documentation Effectiveness

How do you know if documentation is working?

Metrics

1. Support ticket reduction: Are users asking questions docs should answer? Track repeat questions—these indicate documentation gaps.

2. Search queries: What are users searching for? High-volume searches reveal what users need. No results = documentation gap.

3. Page views and time-on-page: Which pages get read? Which get bounced immediately (signal of wrong content or poor quality)?

4. Feedback: "Was this helpful?" buttons, comment forms. Direct user input on what works and what doesn't.

5. Adoption velocity: Time from user signup to first successful integration. Good docs accelerate this.

Qualitative Assessment

User interviews: Watch someone unfamiliar with your product try to use it with only documentation. Where do they get stuck? What questions arise?

Support team insights: Support engineers know where docs fail—they answer the same questions repeatedly.

Onboarding new hires: How long does it take new engineers to become productive? Good internal docs accelerate onboarding.

Conclusion: Documentation as Product

Core insight: Documentation isn't afterthought or checkbox—it's part of the product. Users experience your product through UI and documentation. Bad documentation makes good products unusable.

Documentation that works:

  • Serves user needs: Organized by tasks, not architecture. Answers questions users actually have.
  • Is accurate and maintained: Synchronized with code through docs-as-code practices.
  • Includes all types: Tutorials for learning, how-tos for tasks, reference for lookup, explanations for understanding.
  • Uses clear language: No unnecessary jargon, active voice, short sentences, examples.
  • Is discoverable: Clear navigation, powerful search, direct links.
  • Evolves with product: Versioned, updated every release, treated as engineering work.

Documentation that fails:

  • Exists to check box: Written once, never updated, organized for developer convenience.
  • Ignores user perspective: Assumes knowledge, uses jargon, no practical examples.
  • Incomplete or inaccurate: Missing details, outdated info, code examples don't work.

Investment in documentation pays off:

  • Faster adoption: Users integrate successfully in hours instead of days.
  • Lower support costs: Self-service reduces support volume.
  • Better user experience: Confidence and trust from knowing how things work.
  • Competitive differentiation: When products are similar, documentation quality becomes deciding factor.

Stripe's documentation isn't exceptional because it's comprehensive—many products have comprehensive docs that nobody uses. It's exceptional because it's useful—organized around user needs, filled with working examples, accurate, and maintained. That's the standard. Not aspirational, but achievable with disciplined practice treating documentation as essential engineering work, not optional overhead.

Word count: 5,048 words

References

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  3. Open API Initiative. (2021). OpenAPI Specification v3.1.0. Retrieved from https://spec.openapis.org/oas/v3.1.0
  4. Raman, T. V. (1994). Audio System for Technical Readings. Springer-Verlag. DOI: 10.1007/978-3-642-78894-0
  5. Redish, J. (2012). Letting Go of the Words: Writing Web Content that Works (2nd ed.). Morgan Kaufmann.
  6. Stripe, Inc. (2023). Stripe API Documentation. Retrieved from https://stripe.com/docs/api
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  8. Zinsser, W. (2006). On Writing Well: The Classic Guide to Writing Nonfiction (30th anniversary ed.). Harper Perennial.

Tags

documentation technical-writing knowledge-management work-skills developer-tools

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