Clear docs for clearer skies
Overview
Summary
I designed API documentation that helps developers integrate weather data without the usual headaches. By researching existing weather APIs and identifying common frustrations, I created docs that are both technically accurate and genuinely helpful.
My role
I handled all aspects of this documentation project, from researching developer needs to writing error messages that truly help. This included information architecture, voice strategy, and making complex technical concepts accessible to experts and beginners alike.
The problem
Most weather API documentation feels like it was written by machines, for machines. After analyzing OpenWeatherMap, Tomorrow.io, and WeatherAPI, I found developers struggling with the same issues: buried error handling, confusing organization, and examples that didn't match real-world use cases.
The documentation technically had all the information, but finding and understanding it took too long.
The solution
I restructured everything around how developers work. Error messages now include what went wrong, why, and how to fix it. Real examples replaced generic syntax. The voice stayed technical enough to be credible but human enough to be helpful.
No more hunting through pages for error codes. No more guessing what parameters actually do. Just clear paths from API key to working weather data.
Process
Research & Discovery
User personas
Through analysis of developer forums and API documentation discussions, I discovered three distinct user personas.
(click below to view)
Competitive analysis
After identifying developer needs, I analyzed how current weather APIs fail to meet them. Here is a comparison table of the three main weather APIs developers are using.
(Click the table to view)
Key research insights
Different developers have different needs
Pragmatist needs quick implementation
Optimizer needs specific metrics
Integrator needs reliability documentation
Current docs fail all three personas
Too complex for Pragmatists
Too vague for Optimizers
Too shallow for Integrators
Universal gaps across all APIs:
Limited to no progressive disclosure
Buried or missing error handling
Lack of real-world examples
No use-case driven organization
The core insight:
Developers want to understand the ‘why,’ not just the ‘how.’ They need context for their decisions.
Information Architecture
Structuring for developer success
I organized the documentation around the developer journey—from discovering the API to scaling in production. Each section addresses what developers need at that specific stage, including icons to show which persona uses which stage most.
The structure uses progressive disclosure: scannable overviews, readable explanations, and deep technical details. This serves developers who need quick answers and those implementing complex integrations.
Error handling comes during the troubleshooting phase, exactly when developers need it most. My research showed it causes the most support tickets.
(Click IA flowchart to view)
Voice and tone strategy
Technical accuracy doesn't require technical jargon. I developed voice principles that respect developer intelligence while removing barriers to understanding.
Every word choice supports the same goal: to help developers understand quickly and implement confidently.
Helpful, not hand-holding
Respect developer intelligence
"First, you'll want to make sure you have an API key" — No
"Getting started requires an API key (free registration)" — YES!
Anticipatory
Address the "what ifs" before they ask
Added "What happens when..." sections
Included edge cases in examples
Provided solutions alongside problems
Precise but friendly
Technical accuracy with human touch
"The system will return a 429 status code" — No
"You'll get a 429 error. The API will tell you when you can make requests again" — YES!
Content Design Decisions
Making errors helpful
Since about 75% of support discussions were error-related, I transformed error handling from a problem into a solution.
Traditional API errors tell developers what went wrong but leave them guessing how to fix it. I redesigned every error message to include three things: what happened, why it happened, and exactly how to fix it. This approach turns frustrating dead ends into clear next steps. (Click error messages to view).
Beyond individual messages, I restructured how errors appear in the documentation itself—creating a dedicated section that developers can reference quickly when debugging.
Error message redesign:
Error documentation:
Before: Errors scattered throughout docs or missing
After: Dedicated error section with common causes and fixes
Every error now includes what went wrong, why, and how to fix it.
Using real examples
Generic syntax doesn't teach implementation. I replaced abstract examples with actual use cases.
When developers see the ‘before’ example, they have to figure out what goes where, which is extra work they shouldn't need to do. I rewrote examples to show exactly what they would type—real cities, actual metrics, complete responses.
Now developers can copy, modify, and ship instead of translating abstract concepts into working code.
Endpoint documentation
Build complexity gradually
I structured examples to grow with developer confidence.
Not every developer needs every feature on day one. The Pragmatist wants something working quickly. The Optimizer digs deeper for specific functionality. The Integrator needs error handling and edge cases.
I organized examples in three levels. Developers can stop wherever meets their needs, or continue as their implementation grows.
Simple: Just the basics
Intermediate: Add parameters and handle responses
Advanced: Implement caching and error recovery
Simplifying the complex
Developers shouldn't need a decoder ring to understand API responses. I simplified parameter names, added context to raw data, and streamlined complex processes. Each decision reduced cognitive load while maintaining technical accuracy.
Parameter names
Before:
temp_unit, loc, precip_prob
After:
temperature_units, location, chance_of_rain
Response context
Before: Raw numbers without meaning
After: Added categories and recommendations
Quick start
Before: Multiple pages of setup instructions
After: Three numbered steps with success confirmations
Outcomes & Learnings
What I achieved:
Documentation organized around the developer journey
Error messages that help developers fix problems
Progressive disclosure respecting different skill levels
Real-world examples teaching actual implementation
Key learnings:
This project transformed how I think about technical writing. Prioritizing developer needs shaped this documentation in ways that truly serve its users.
Writing for developers requires balancing technical accuracy with human understanding. Every word choice matters, and the best documentation anticipates problems before they arise.
The major insight was that developers need better documentation. Clear organization, helpful error messages, and real examples can transform frustrating integrations into smooth experiences.