API Developer Experience: Endpoints, Errors & Auth
Measures developer satisfaction with API endpoint clarity, error diagnostics, and authentication workflows to identify friction points and inform API roadmap priorities.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
In the past 30 days, how often did you interact with our API?
- Daily
- Several times a week
- Weekly
- Once or twice
- Not in the past 30 days
How clear is the URL structure and naming of our API endpoints?
In the past 30 days, approximately how often did you encounter an API error response?
- Never
- Rarely (1–2 times)
- Sometimes (3–5 times)
- Often (6 or more times)
- Not sure
Which authentication methods did you use or configure in the past 30 days? Select all that apply.
- API key
- OAuth 2.0 — Client Credentials
- OAuth 2.0 — Authorization Code
- JWT / Service account
- HMAC or request signing
- No auth changes in the past 30 days
- Not sure
Overall, how would you rate the clarity of our API (documentation, endpoints, and error messages combined)?
Based on your responses in this survey, please share any additional thoughts or suggestions about your API experience.
Which of the following best describes your primary role?
- Backend engineer
- Frontend engineer
- Full-stack developer
- DevOps / SRE
- Data engineer
- Product manager
- QA / Tester
- Student
- Other
Thank you for your time. Your feedback directly helps us improve the API experience for all developers.
Which of the following activities did you perform with the API in the past 30 days? Select all that apply.
- Reading/fetching data
- Creating or updating records
- Managing authentication/authorization
- Configuring webhooks/callbacks
- Bulk or asynchronous jobs
- Building or maintaining integrations/SDKs
- Testing or troubleshooting
- Other
How clear are the required and optional request parameters (headers, query params, body)?
Which of the following error types did you encounter? Select all that apply.
- Validation errors (4xx)
- Authentication failures (401)
- Permission/scope issues (403)
- Resource not found (404)
- Rate limiting (429)
- Timeouts or network errors
- Server errors (5xx)
- Deprecation or version errors
- Other
How easy or difficult was it to generate or rotate API keys or credentials?
How likely are you to recommend our API to a fellow developer?
How long have you been working with web APIs?
- Less than 1 year
- 1–2 years
- 3–5 years
- 6–9 years
- 10+ years
How clear are the response formats and field definitions returned by our endpoints?
How helpful were the API error messages in diagnosing and resolving issues?
How easy or difficult was it to set up an OAuth flow (authorization code or client credentials)?
Rank the following areas by how much improvement would benefit your workflow (drag the highest-impact area to the top).
- Endpoint clarity
- Error message quality
- Authentication flows
- API documentation
- SDKs & developer tools
What is the approximate size of your organization?
- 1–10
- 11–50
- 51–200
- 201–1,000
- 1,001–5,000
- 5,001+
How clear is the pagination behavior (cursors, offsets, page limits) across endpoints?
If you recall a particularly unhelpful error message, please paste it or summarize it briefly.
How easy or difficult was it to manage token refresh or session handling?
Which programming languages or platforms do you primarily use with our API? Select all that apply.
- JavaScript / TypeScript
- Python
- Java
- Go
- Ruby
- C# / .NET
- PHP
- Other
Name one endpoint or area of the API that felt unclear and briefly explain why.
Describe any authentication blockers or confusing steps you encountered.
In which region are you primarily located?
- North America
- Europe
- Asia
- Latin America
- Middle East
- Africa
- Oceania
- Prefer not to say
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
How it compares
We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.
Why this template
- AI follow-ups automatically explore the 'why' behind concept reactions, replacing shallow rating-only templates
- Academic-grade methodology with proper scale construction—no leading questions or attention checks that bias results
- Full reproducibility: every AI prompt, model parameter, and logic branch is logged and visible for replication studies
SurveyMonkey
Product Testing Survey TemplateA well-established template used over 57,000 times, focused on gathering feedback on product concepts, messaging, and pricing. Expert-certified and customizable, but fundamentally a static questionnaire with no adaptive follow-up capabilities.
What it does well
- Used 57,000+ times with high social proof and trust
- Expert-certified questions covering credibility, innovation, value, and quality dimensions
- Supports AI-generated word clouds and basic sentiment analysis on results
Where it falls short
- No AI-powered follow-up questions to explore why respondents rate concepts the way they do
- AI features limited to post-hoc analysis (word clouds, segments)—not real-time interview adaptation
- Black-box AI insights with no transparency into models or prompts used
- Expensive Audience panel upsell for reaching target respondents
Qualtrics
Product Research Survey TemplateA Ph.D.-designed concept testing template with sophisticated methodology including monadic and sequential monadic designs. Enterprise-grade but expensive and complex, with no AI interview follow-ups during data collection.
What it does well
- Ph.D.-designed methodology with monadic and sequential monadic testing options
- Advanced analytics including conjoint analysis and Stats iQ for deeper data exploration
- Pre-built dashboards and automatic reporting for concept comparison
Where it falls short
- Enterprise pricing makes it inaccessible for academic researchers and small teams
- No AI-driven conversational follow-ups during the survey itself
- High learning curve due to feature complexity
- No transparency into how AI-powered analytics arrive at their conclusions
Typeform
New Product Survey TemplateA visually appealing, conversational-style template that asks one question at a time. Great UX for respondents but limited to static branching logic—no AI that adapts questions based on prior answers in real time.
What it does well
- Elegant one-question-at-a-time design that boosts completion rates
- Highly customizable branding with 300+ integrations
- Logic Jumps allow basic conditional branching
Where it falls short
- No AI follow-up capability—branching is pre-defined, not adaptive
- Limited analytics; designed more for data collection than deep research analysis
- Advanced logic jumps restricted to paid plans
- No built-in methodology guidance for proper concept testing scale construction
Jotform
New Product Survey Form TemplateA free, drag-and-drop template focused on collecting basic customer feedback before product release. Easy to set up but methodology is generic and lacks any AI or adaptive research capabilities.
What it does well
- Completely free to use with drag-and-drop customization
- 100+ third-party integrations including Google Sheets and CRM systems
- QR code sharing and multi-device support
Where it falls short
- No AI-powered follow-up questions or conversational capabilities
- Generic question design with no survey methodology guidance
- No concept testing methodology (monadic/sequential) built in
- Basic analytics with no advanced statistical tools
SurveySparrow
Product Feedback Survey TemplateA conversational-style product feedback template with multi-language support and in-app survey embedding. Focuses on general product feedback rather than structured concept evaluation methodology.
What it does well
- Conversational UI with 130+ language support via Google Translate
- Skip/display logic for personalized survey paths
- In-app and offline survey collection capabilities
Where it falls short
- No AI-driven adaptive follow-up during the survey experience
- General product feedback focus—not purpose-built for concept evaluation research
- AI features limited to survey creation assistance, not real-time interview probing
- No transparency into AI model or prompt decisions
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.