Quality loop: measure answer quality, and prove it improved.
An AI platform is only productive once you know how well it answers. RAGSuite’s quality loop joins citations, your team’s feedback, model comparison and query tracing into a cycle that makes answers better step by step.
Basics in every plan · full in the RAGSuite Engine and Enterprise
Three months to the end of a quarter. Price increases above 5% allow special termination.
The notice period is three months to the end of a quarter.
How does the quality loop work?
The loop has four steps and runs in every plan. The more your team rates, the faster you see which content is missing or outdated, and whether a change really helped.
Answers with sources
Every answer shows the passages it comes from: checkable by anyone.
Collect feedback
Thumbs, stars, reasons and comments show where answers miss.
Find the cause
Query tracing shows whether the search, the source or the model was the problem.
Improve and prove
Add the source, compare models, see the effect in the dashboard.
What your team can say about every answer.
Thumbs
Helpful or not: one click under every answer.
Stars
A rating from 1 to 5 for finer differences.
Reasons
For example “outdated”, “incomplete” or “wrong source”.
Comment
One sentence on what was missing: straight to whoever maintains the content.
One question, several models: scored side by side.
With model comparison you ask the same question to several saved configurations: large models from leading providers, EU-based providers or local models. You see quality, token use and response time side by side and decide with data which model fits which project.
The Community Edition shows a preview; model comparison is fully available in the RAGSuite Engine and Enterprise.
Every query in detail: retrieval, reranking, model, output.
Query tracing breaks every query into its steps: retrieval, reranking, language model and output, with timings, parameters and a confidence indication. So you see whether a weak answer came from a missing source or the model. Export as CSV and JSON.
Usage, response time, satisfaction and cost.
The dashboard shows how much people ask, how fast RAGSuite answers, how satisfied your team is and what it costs: queries, response times, share of positive ratings and tokens. The basic dashboard comes with every plan; trends, cohorts and cost breakdowns are added in the RAGSuite Engine and Enterprise.
What is in which plan?
| Feature | Community | RAGSuite Engine | Enterprise |
|---|---|---|---|
| Source on every answer, feedback | ✓ | ✓ | ✓ |
| Basic dashboard and history | ✓ | ✓ | ✓ |
| Model comparison | Preview | ✓ | ✓ |
| Query tracing with CSV/JSON export | – | ✓ | ✓ |
| Advanced analytics | – | ✓ | ✓ |
| Export feedback as CSV | – | ✓ | ✓ |
Questions about the quality loop.
What is the quality loop?
The quality loop is the cycle you use to measure and improve the quality of AI answers in RAGSuite: answers with sources, your team’s feedback, finding causes with query tracing, and proving improvements with model comparison and the dashboard. It turns a demo into a system your team trusts day to day.
What is in the Community Edition?
The Community Edition includes sources on every answer, feedback with thumbs, stars and reasons, the basic dashboard and history. Model comparison shows as a preview. Query tracing, advanced analytics and exports come with the RAGSuite Engine and Enterprise, which have the full feature set.
How does model comparison work?
You ask one question to several saved model configurations and see the answers side by side, scored on quality, token use and response time. That lets you decide per project whether a smaller, cheaper model is enough or a large one is needed. That saves time and computing power.
What does a query trace show?
A trace shows every step of a query: which passages were found and how they were weighted, which model answered with which parameters, how long each step took, plus a confidence indication. Traces can be exported as CSV or JSON, for example for your quality assurance.
Can anyone see which employees gave bad ratings?
Feedback serves answer quality, not performance monitoring. You can switch conversation storage off and set deletion periods. You decide what is stored and analysed, which makes agreement with the works council and data protection easier. We describe the rules on our data protection page.
Answer quality you can prove.
See model comparison and query tracing in a demo, or try it yourself in the RAGSuite Engine.