Fact-Checking RAG Answers with the Groundedness Score
Blog post from deepset
The Groundedness Observability feature in deepset Cloud tracks the degree to which an answer generated by a retrieval augmented generation (RAG) system is based on the underlying documents, providing a quantifiable score for assessing the quality and error sources of LLM-powered prototypes. This feature aims to improve security and trust among users and builders by identifying hallucinations and ensuring that answers are grounded in data. The Groundedness Observability Dashboard provides insights into pipeline performance, allowing users to compare different models, optimize prompts, and refine retrieval setups. Additionally, the feature offers a Reference Predictor, which enables users to verify answers with academic-style citations, promoting confidence in LLM-powered products and paving the way for widespread adoption in production environments.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| LLM | 29 | 2,790 | 311 | 123 | +34% |
| RAG | 16 | 1,418 | 170 | 60 | +93% |
| Observability | 6 | 1,376 | 249 | 90 | +15% |
| AI Model Fine-tuning | 1 | 444 | 125 | 69 | +22% |
| Vector Search | 1 | 1,728 | 228 | 84 | +63% |
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