Datumo Eval is an AI reliability evaluation platform designed to quantify and monitor the quality of LLM responses. It offers a wide range of evaluation tools to help teams build more trustworthy AI.
At the core of the platform is a structured evaluation framework, organized around a simple question: What are we evaluating?
Based on this, Datumo Eval defines two main types of evaluation, each broken down into detailed categories of metrics.
Evaluation metrics allow us to:
Objectively measure the quality of an AI model
Detect potential issues early during real-world deployment
Build a sustainable framework for continuous improvement
Clear standards help teams assess performance consistently and accurately. With the right metrics, it’s easier to identify what needs improvement and take focused action.
In Datumo Eval, metrics are grouped into two types: Basic Evaluation and RAG Checker Evaluation. Each type serves a different purpose and can be used selectively depending on the model’s application.
1. Basic Evaluation
This type focuses on overall response quality. It is designed to assess user experience, ethical behavior, and information accuracy.
Safety Evaluation
Checks whether a model’s response contains harmful, biased, or socially sensitive content such as hate speech, illegal advice, or personal attacks.
Key indicators include:
Illegality
Personal or sensitive references
Bias or discrimination
Hate or offensive language
Controversial or harmful claims
Use cases:
Highly recommended for domains like public services, financial advice, or customer support, where sensitive content must be carefully filtered.
RAG Quality Evaluation
When there’s no clear ground-truth answer, this evaluation helps determine whether the response is logically sound and informative, regardless of retrieval.
Evaluation methods:
Likert-scale scoring (qualitative)
Text Decomposition scoring (quantitative, scale of 0 to 1)
Core metrics include:
Clarity of reasoning
Contextual relevance
Answer relevance
Factual accuracy
Information completeness
Evaluation is not just a final checkpoint. It’s a feedback loop that drives better AI. With a clear understanding of what to measure and how to interpret results, teams can build models that are not only smarter, but safer and more aligned with real-world needs.
2. RAG Checker Evaluation
This evaluation focuses on responses generated through Retrieval-Augmented Generation (RAG). It automatically assesses how factually consistent a response is with the retrieved documents used during generation. In other words, it checks whether the model’s output is grounded in actual source material or includes hallucinated content.
Key Evaluation Questions:
Is the response grounded in the retrieved context?
How much hallucination is present in the output?
Techniques Used:
Text Decomposition
Entailment Analysis
Claim Matching
These methods help quantify the factual accuracy of RAG-generated responses and ensure that the model is relying on real, verifiable information rather than fabricating details.
Datumo Eval helps teams evaluate and manage LLM quality and reliability from multiple angles using structured metrics.
What sets it apart?
Datumo Eval uses a multi-agent system where specialized AI agents collaborate to generate sharp, targeted evaluation questions—enabling deeper, more accurate assessments of your model’s performance.
If you’re using LLMs or building services powered by them, Datumo Eval can help you raise the bar for quality and trust.
