As AI adoption continues to accelerate, organizations are increasingly relying on Large Language Models (LLMs) to power intelligent applications—from chatbots to copilots to knowledge assistants. But building an AI system is just the beginning. One of the most critical, yet often overlooked, aspects of the AI development lifecycle is evaluation: understanding how well your system performs, identifying failure modes, and making informed decisions about models, prompts, and architecture.
This becomes even more crucial when working with Retrieval-Augmented Generation (RAG) systems, where a language model is combined with an external knowledge source to improve relevance and reduce hallucinations. RAG systems introduce new variables—such as document retrieval quality and grounding consistency—that make traditional evaluation methods insufficient.
In this post, we’ll walk through how to evaluate AI systems, particularly RAG-based architectures, using the .NET ecosystem and the Microsoft.Extensions.AI.Evaluation library. We'll cover evaluation criteria, challenges unique to RAG systems, and how to build a structured, repeatable evaluation pipeline to gain deep insights into your AI’s behavior.
When we talk about evaluating AI systems, we’re not just referring to precision, recall, or BLEU scores. Those traditional metrics—while still useful in certain contexts—fail to capture the full complexity of interactions with modern LLMs, especially in retrieval-augmented generation (RAG) systems.
Evaluation in this context is about qualitatively assessing how well the AI system meets user expectations. It’s about answering questions like:
These questions go beyond pure model performance and into the realm of end-to-end system quality.
For example, you might have a great LLM, but if the retrieved documents aren’t relevant, the output will still be poor. Or you might get a factually correct answer that’s hard to read, incoherent, or misleading because of subtle phrasing issues.
That’s why evaluation must be holistic, considering not just what the model outputs, but also how it got there—and whether it makes sense from a user and business perspective.
In practice, this means defining a clear set of evaluation criteria, applying them consistently, and analyzing the results across different configurations, models, or prompts. It’s not just about getting a score—it’s about building confidence in your AI system.
To evaluate LLM-based systems effectively, especially those using RAG, we need to go beyond accuracy and look at qualitative dimensions of the responses.
Each of these dimensions offers a different lens for understanding system quality, and many evaluation tools—including the one we’ll explore later—support them natively.
While evaluation is essential for all AI systems, RAG introduces unique complexities:
In short: evaluation in RAG systems must go deeper than “was this a good answer?” It must explore why the answer was good (or bad), and what part of the system is responsible.
The quality of your evaluation is only as good as your dataset.
In our case, we manually crafted a dataset of queries and expected answers with the help of domain experts inside the organization. These experts deeply understand the company’s documentation, business logic, and terminology—making them ideal partners for defining what “correct” looks like.
We also explored (but haven't yet used) tools that generate synthetic evaluation datasets directly from your vector index. These can be a good starting point when manual resources are limited. Some tools worth looking into:
However, nothing beats expert judgment when your use case requires precision or has compliance implications.
Microsoft has introduced a flexible evaluation framework as part of the Microsoft.Extensions.AI package ecosystem, built specifically for .NET developers.
Microsoft.Extensions.AI.Evaluation provides tools to define evaluation scenarios, apply both built-in and custom evaluators, and generate structured reports you can analyze or visualize later.
One of the pillars of our product strategy is not only to provide the best Intelligent Knowledge Platform with powerful AI features, but also to ensure they are easy to integrate, validate, and operate in real-world environments. That’s why we created Atlas CLI—an internal command-line tool designed to simplify and automate various operations across our Atlas Fuse platform.
Atlas CLI is envisioned as the go-to interface for developers, QA engineers, and DevOps teams who need to interact programmatically with Atlas. Whether it’s checking the health status of the platform, triggering evaluation runs, or—soon—managing knowledge collections, uploading datasets, or initiating training workflows, this CLI is a key component for streamlined and professional AI operations.
Currently, one of the most powerful commands is evaluate, which enables automatic evaluation of a dataset of questions and expected answers. It compares ground-truth expectations with actual outputs from Atlas Fuse, and generates multiple outputs:
All of this can be triggered from a single command:
atlas fuse evaluate ./data/ground_truth.json -k <KnowledgeCollectionId> -o ./report -d
Through applying this framework internally, we’ve learned:
In the race to build AI-powered apps, it’s tempting to jump from prototype to production. But without rigorous evaluation, you’re flying blind. For systems that use LLMs—and especially RAG—quality is nuanced and multi-dimensional.
By applying structured evaluation with tools like Microsoft.Extensions.AI.Evaluation, and grounding your process in expert-labeled datasets, you can build AI systems that are not only powerful, but trustworthy.
Evaluation isn't a final checkbox—it’s an ongoing process that enables better architecture, better decisions, and ultimately, better user experiences.