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.
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AI-Powered Questions
Intelligent follow-up questions based on responses
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Real-time sentiment and insight detection
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Detailed Reports
Comprehensive insights and recommendations
Template Overview
36
Questions
AI-Powered
Smart Analysis
Ready-to-Use
Launch in Minutes
This professionally designed survey template helps you gather valuable insights with intelligent question flow and automated analysis.
Sample Survey Items
Q1
Chat Message
Welcome! Thank you for participating in this survey about your experience with Retrieval-Augmented Generation (RAG) systems.
This survey takes approximately 6–8 minutes. Your responses are completely anonymous, reported only in aggregate, and used for internal research purposes. There are no right or wrong answers — we are interested in your honest opinions and experiences. Participation is voluntary, and you may stop at any time.
Q2
Multiple Choice
Have you built or maintained a RAG system in the last 6 months?
Yes
No
Not sure
Q3
Multiple Choice
Which role best describes your day-to-day work?
ML engineer
Backend engineer
Data scientist
MLOps/Platform
Product engineer
Researcher
Architect/Tech lead
Other
Q4
Multiple Choice
Which content sources feed your retriever today? Select all that apply.
Proprietary documents
Code repositories
Product knowledge base
Web crawl
Vendor API docs
Slack/Chat logs
Support tickets
Wiki/Confluence
Database/warehouse
Not applicable
Other
Q5
Opinion Scale
In the last 30 days, how well did retrieved context meet your task requirements?
Range: 1 – 7
Min: Far below needsMid: NeutralMax: Far above needs
Q6
Multiple Choice
How do you set or tune top-k and related retrieval parameters?
Manual experimentation
Grid/Random search
Bayesian optimization
Vendor auto-tuning
Learned retrieval policy
Not tuned
Other
Q7
Opinion Scale
In the last 30 days, how often did you encounter irrelevant or off-topic passages in retrieval results?
Range: 1 – 5
Min: NeverMid: NeutralMax: Very often
Q8
Opinion Scale
In the last 30 days, how often did you encounter missing context (key information not retrieved)?
Range: 1 – 5
Min: NeverMid: NeutralMax: Very often
Q9
Opinion Scale
In the last 30 days, how often did you encounter stale or outdated content in retrieval results?
Range: 1 – 5
Min: NeverMid: NeutralMax: Very often
Q10
Opinion Scale
In the last 30 days, how often did you encounter duplicate or near-duplicate chunks in retrieval results?
Range: 1 – 5
Min: NeverMid: NeutralMax: Very often
Q11
Multiple Choice
How are model answers grounded or cited in your RAG system? Select all that apply.
Inline citations with URLs
Inline citations with document IDs
Evidence block after the answer
Tool outputs included verbatim
Structured JSON evidence list
No grounding/citations
Other
Q12
Opinion Scale
Over the last 30 days, how much do you trust the correctness of cited evidence in your RAG system's outputs?
Range: 1 – 7
Min: No trust at allMid: NeutralMax: Complete trust
Q13
Ranking
Rank your top 3 preferred citation/grounding display styles.
Drag to order (top = most important)
Inline per sentence
Numbered endnotes
Collapsible evidence panel
Top-k sources with scores
Link to full passages
Show only on demand
Q14
Opinion Scale
In the last 30 days, how frequently did you observe hallucinations in your RAG outputs despite grounding?
Range: 1 – 5
Min: NeverMid: NeutralMax: Very frequently
Q15
Long Text
Please describe a recent grounding failure you encountered and its impact on your work.
Max chars
Q16
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.
AI InterviewLength: 2Personality: [Object Object]Mode: Fast
Reference questions: 4
Q17
Multiple Choice
Which evaluation tools or libraries do you use for RAG? Select all that apply.
Ragas
TruLens
DeepEval
Promptfoo
Custom harness
LlamaIndex evals
None
Other
Q18
Multiple Choice
Which metrics best reflect your RAG quality today? Select all that apply.
Precision@k
Recall@k
MRR
nDCG
Answer faithfulness
Context precision/recall
Groundedness score
Human ratings
Production usage signals
Custom internal metrics
Q19
Long Text
If you use a custom metric, please briefly describe it and how you compute it.
Max chars
Q20
Dropdown
How automated is your evaluation workflow?
None (manual only)
Some scripts
CI-integrated checks
Continuous eval in production
Q21
Dropdown
How often do you run RAG benchmarks?
Before each release
Weekly
Biweekly
Monthly
Quarterly
Ad hoc only
Q22
Multiple Choice
What is the primary programming language you use for RAG development?
Python
JavaScript/TypeScript
Java
Go
C
Rust
Other
Q23
Multiple Choice
What is your primary vector store or retriever backend?
Pinecone
Weaviate
Milvus
FAISS
Elasticsearch/OpenSearch
pgvector
Chroma
Vespa
Not applicable
Other
Q24
Multiple Choice
Which embedding model is your primary choice?
OpenAI text-embedding-3
OpenAI small embedding
Cohere Embed
VoyageAI
Jina embeddings
E5 family
Instructor
BGE family
Local model
Other
Q25
Multiple Choice
Do you use a reranker after initial retrieval?
Yes
No
Experimenting
Q26
Multiple Choice
Which reranker do you use most often?
Cohere Rerank
Voyage Rerank
Jina Reranker
Cross-encoder (e.g., MS MARCO)
Self-hosted reranker
Other
Q27
Dropdown
What is your end-to-end RAG latency target per query?
< 200 ms
200–500 ms
500 ms–1 s
1–2 s
2–5 s
> 5 s
No specific target
Q28
Opinion Scale
How critical is retrieval quality to the overall success of your RAG system?
Range: 1 – 7
Min: Not at all criticalMid: NeutralMax: Extremely critical
Q29
Opinion Scale
How satisfied are you with your RAG system overall today?
Range: 1 – 7
Min: Not at all satisfiedMid: NeutralMax: Extremely satisfied
Q30
Ranking
Rank your top 3 priorities for improving your RAG system in the next 3 months.
Drag to order (top = most important)
Improve retrieval precision/recall
Better grounding/citations
Reduce latency
Lower cost per query
Scale to more data sources
Harden evaluation pipeline
Security/compliance
Developer ergonomics
Q31
Long Text
Based on your responses in this survey, please share any additional thoughts or experiences about your RAG retrieval or grounding challenges.
Max chars
Q32
Multiple Choice
How many years of professional experience do you have in software, data, or ML?
0–1
2–4
5–9
10–14
15+
Prefer not to say
Q33
Multiple Choice
In which region do you primarily work?
Africa
Asia
Europe
North America
Oceania
South America
Prefer not to say
Q34
Multiple Choice
What is the approximate size of your organization (number of employees)?
1–10
11–50
51–200
201–1,000
1,001–5,000
5,001+
Prefer not to say
Q35
Multiple Choice
What is the primary industry or domain for your RAG work?
Technology
Finance
Healthcare/Life sciences
Retail/CPG
Education
Government/Public sector
Manufacturing
Media/Entertainment
Other
Prefer not to say
Q36
Chat Message
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.
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