Q01
Long Text
All templates
RAG System Quality & Grounding Assessment (Developer Survey)
Evaluates developer experiences with Retrieval-Augmented Generation systems across retrieval quality, grounding accuracy, evaluation practices, and infrastructure. Designed for ML engineers, backend engineers, and data scientists actively building or maintaining RAG pipelines.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
36 questions · ~4 min
Q02
Multiple Choice
Have you built or maintained a RAG system in the last 6 months?
Q03
Multiple Choice
Which role best describes your day-to-day work?
Q04
Multiple Choice
Which content sources feed your retriever today? Select all that apply.
Q05
Multiple Choice
How are model answers grounded or cited in your RAG system? Select all that apply.
Q06
Multiple Choice
Which evaluation tools or libraries do you use for RAG? Select all that apply.
Q07
Multiple Choice
What is the primary programming language you use for RAG development?
Q08
Long Text
How critical is retrieval quality to the overall success of your RAG system?
Q09
Multiple Choice
How many years of professional experience do you have in software, data, or ML?
Q10
Long Text
Thank you for completing this survey! Your input is valuable and will help improve RAG systems and developer tooling. All results will be reported in aggregate only.
Q11
Long Text
In the last 30 days, how well did retrieved context meet your task requirements?
Q12
Long Text
Over the last 30 days, how much do you trust the correctness of cited evidence in your RAG system's outputs?
Q13
Multiple Choice
Which metrics best reflect your RAG quality today? Select all that apply.
Q14
Multiple Choice
What is your primary vector store or retriever backend?
Q15
Long Text
How satisfied are you with your RAG system overall today?
Q16
Multiple Choice
In which region do you primarily work?
Q17
Multiple Choice
How do you set or tune top-k and related retrieval parameters?
Q18
Long Text
Rank your top 3 preferred citation/grounding display styles.
Q19
Long Text
If you use a custom metric, please briefly describe it and how you compute it.
Q20
Multiple Choice
Which embedding model is your primary choice?
Q21
Long Text
Rank your top 3 priorities for improving your RAG system in the next 3 months.
Q22
Multiple Choice
What is the approximate size of your organization (number of employees)?
Q23
Long Text
In the last 30 days, how often did you encounter irrelevant or off-topic passages in retrieval results?
Q24
Long Text
In the last 30 days, how frequently did you observe hallucinations in your RAG outputs despite grounding?
Q25
Long Text
How automated is your evaluation workflow?
Q26
Multiple Choice
Do you use a reranker after initial retrieval?
Q27
Long Text
Based on your responses in this survey, please share any additional thoughts or experiences about your RAG retrieval or grounding challenges.
Q28
Multiple Choice
What is the primary industry or domain for your RAG work?
Q29
Long Text
In the last 30 days, how often did you encounter missing context (key information not retrieved)?
Q30
Long Text
Please describe a recent grounding failure you encountered and its impact on your work.
Q31
Long Text
How often do you run RAG benchmarks?
Q32
Multiple Choice
Which reranker do you use most often?
Q33
Long Text
In the last 30 days, how often did you encounter stale or outdated content in retrieval results?
Q34
AI Interview
We'd like to understand more about your experience with grounding and citation quality. An AI moderator will ask you a couple of follow-up questions.
Q35
Long Text
What is your end-to-end RAG latency target per query?
Q36
Long Text
In the last 30 days, how often did you encounter duplicate or near-duplicate chunks in retrieval results?
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.
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Open this template in the editor. Every part is yours to change before the first respondent sees it.