This article will give you tips on how to analyze responses from an API Developers survey about API Documentation Quality using AI survey analysis techniques.
Choosing the right tools for survey response analysis
The way you analyze survey responses from API developers depends heavily on the data’s form—whether it’s structured, structured with open ends, or purely qualitative.
Quantitative data: If your survey includes numerical or choice-based questions (“How would you rate our API documentation from 1-10?”), tools like Excel or Google Sheets make tabulation and simple charting easy. You simply count, average, and visualize the numbers—no need for advanced AI analysis here.
Qualitative data: When you collect open-ended feedback (“What was hardest about understanding our API?”), things get trickier. Reading dozens (or even hundreds) of open responses and trying to summarize them is not just tedious—it’s also prone to personal bias and missed patterns. That’s where AI tools shine: they quickly extract patterns, key ideas, underlying causes, and even sentiment from long-form answers in ways that simple spreadsheet analysis just can't match.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can copy your survey’s qualitative responses and paste them into ChatGPT, then chat about what it all means.
Pros: It’s flexible—you can experiment with different prompts to surface insights, ask follow-ups, or drill into edge cases. For smaller datasets or quick analyses, it’s a good starting point.
Cons: Once you have more responses or want to organize things by specific questions, choices, or even by NPS segment, handling everything in ChatGPT becomes clunky. There’s a lot of manual copying, pasting, and organizing, and it’s hard to keep context straight if you want to return later or collaborate with teammates.
All-in-one tool like Specific
Specific is an AI survey tool built to make conversational, AI-analyzed surveys as seamless as possible. Instead of juggling exports and manual analysis, you can both collect qualitative feedback from API developers and have the results automatically analyzed—no spreadsheets or manual categorization required.
When collecting data with Specific, you can set up the survey to ask intelligent follow-up questions automatically, which boosts the relevance and depth of each response. If someone mentions ambiguous pain points with your API documentation, the AI probes with clarifying questions to get concrete examples. (You can read more about this in our guide to AI follow-up questions.)
For analysis, Specific instantly summarizes all responses, finds key themes, groups similar insights together, and even ranks ideas by frequency—transforming raw data into actionable insights within seconds. You can chat directly with the AI about your survey results just like you would in ChatGPT, but with added features for managing context, filtering by audience, or drilling into individual question threads. Learn how AI survey response analysis works here.
Bottom line: For ad-hoc, one-off questions, your favorite AI chatbot can work. But if you care about managing, organizing, and digging deep into actual developer feedback (especially when you run repeated or follow-up surveys on API documentation quality), a tool purpose-built for survey creation, follow-up, and response analysis (like Specific) is worth a look. If you want to start quickly, check our AI survey generator.
Useful prompts that you can use for analyzing API developers survey about API documentation quality
If you want to extract better insights from your API documentation quality surveys, you’ll get more out of both GPT tools and Specific with a thoughtful prompt strategy. Here are some proven prompts to use—try them in Specific’s analysis chat, or use them elsewhere if you prefer.
Prompt for core ideas: This prompt distills long, open-ended feedback into a clean list of main themes. It works especially well for grouping developer complaints or improvement requests.
Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.
Output requirements:
- Avoid unnecessary details
- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top
- no suggestions
- no indications
Example output:
1. **Core idea text:** explainer text
2. **Core idea text:** explainer text
3. **Core idea text:** explainer text
The more context you provide, the better the output. Try giving a bit of background—what type of API, who your main users are, or your personal goals. Here’s an example prompt:
We surveyed internal and external API consumers to identify what frustrates them about our API documentation. Our goal is to improve first-time integration speed and reduce the number of support tickets.
Once you have core ideas, dig deeper by asking:
Tell me more about “unclear error codes.”
Prompt for specific topic: Quickly check if anyone mentioned a pain point or feature idea you care about.
Did anyone talk about auto-generated code samples? Include quotes.
Prompt for personas: Cluster API developers into key personas, summarizing what differentiates them. (Useful for product or documentation targeting.)
Based on the survey responses, identify and describe a list of distinct personas—similar to how "personas" are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed in the conversations.
Prompt for pain points and challenges: Surface the core problems with your API docs—what blocks API adoption or retention?
Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note any patterns or frequency of occurrence.
You might also try prompts for motivations, sentiment, direct suggestions, or identifying unmet needs—especially helpful given that 94% of developers say documentation quality directly affects their decision to adopt or stick with an API. [3]
Want a ready-to-use survey for this topic? See our piece on best questions to ask in API developer documentation surveys.
How Specific analyzes responses by question type
Specific gives structured summaries according to how the original question was set up:
Open-ended questions (with or without follow-ups): You get a combined summary that pulls out the key patterns, plus a digest of any AI follow-up questions and what those answers revealed.
Choices with follow-ups: Each option in a multiple-choice question comes with its own summary of related follow-up answers, so you see not just what people chose, but why.
NPS (Net Promoter Score): Feedback is analyzed separately for promoters, passives, and detractors, capturing very different types of feedback (enthusiasm, mild critiques, or deal-breaker issues). This is critical, since SmartBear found that only 23% of teams rate their own API documentation as “good”, with just 5% saying it’s “very good” [2]. Looking at NPS by segment helps you spot what delights versus what frustrates.
You can “manually” do the same in ChatGPT, but it’s a lot of copy-paste and maintaining organizational discipline as you explore across question types or segments—something most teams struggle with in practice.
For building a custom workflow, or if you want to edit or update your survey as you go, consider using the AI survey editor to adjust questions easily.
How to tackle challenges with AI’s context limits
Context size is a real problem with GPT models—if you have too many responses from API developers, not all of them will fit at once for analysis (whether in ChatGPT or any AI platform). Specific has two proven solutions for this:
Filtering: You can select which responses to include in your analysis (such as, “Show me only answers from developers who rated our docs lower than a six”). This way, you get a focused summary for just that subset.
Cropping: Only want to look at specific questions (for example, “What made our documentation confusing?”)—slice out just those data for the AI to process, so you stay within the context limit. This lets you analyze even large surveys with hundreds of developer comments accurately.
Don’t forget, you can also fine-tune your survey workflow for API developers to minimize noise and increase the relevance of collected feedback.
Collaborative features for analyzing API developers survey responses
Collaboration between product, engineering, and developer relations teams is crucial when analyzing complex feedback on API documentation—but it’s rarely easy in most tooling.
In Specific, you can analyze survey data just by chatting with AI. This makes it far easier for multiple people to ask their own questions about the data or explore emerging ideas.
Multiple analysis chats are supported. Each conversation can have its own filters or focus—let one person dig into pain points from external developers, while another explores feedback from internal teams; everything is organized and attributed.
See who said what: Each chat thread in the analysis UI clearly shows who created each conversation and displays the sender’s avatar, making collaboration across teams transparent. This is perfect for API documentation projects that span technical writers, product managers, and actual developers as stakeholders.
With this structure, analysis is not a black box—anyone contributing to your API documentation quality initiative can follow up, ask new questions, or share the chat with others. To see how this looks in practice, check out our AI survey response analysis workflow.
Create your API developers survey about API documentation quality now
Kick-start your documentation improvement process—get actionable insights by creating a smart, conversational survey with Specific’s AI-driven builder, then analyze responses instantly as a team.