If you've worked with LLMs, you’ve probably experienced this: You craft what seems like the perfect prompt, test it a few times, and everything looks great. Then you put it into production, and suddenly your users are getting responses that range from slightly off to wildly hallucinated.

What happened?

Welcome to the world of non-deterministic AI systems, where the same input can produce different outputs each time. Unlike traditional software where bugs are reproducible and fixes are verifiable, LLMs introduce a level of unpredictability that makes traditional testing approaches fall short.

This is where LLM evaluation frameworks come in.

In this article, we'll cover what makes a good LLM evaluation framework, why you absolutely need one if you're working with AI, and how to choose the right one for your needs.

Why do you need one?

An LLM evaluation framework is a tool that helps you test how well your LLM components are working. Think of it like this: if traditional software testing is about making sure your code does exactly what you expect every time, LLM evaluation is about making sure your AI system behaves within acceptable parameters almost every time.

You can use an eval framework to check if a prompt pulls the right context from your RAG setup, if the model's response matches what you expect, or if the output is valid JSON. This is important because LLMs are non-deterministic. You need to perform stress tests before you put your system into production.

For example, if you're building a customer support AI that needs to extract order numbers from customer messages. With an evaluation framework, you can test thousands of variations of "Where's my order #12345?" to make sure your system correctly detects the order number regardless of how the question is phrased.

How to Evaluate an Eval Framework

At their core, LLM evaluation frameworks consist of several key components that work together to give you a complete picture of your AI system's performance. Let’s talk about each of them, and give you some ideas on how to decide what you need.

1/ Test Datasets

This dataset is just a list of good question/ answer pairs that you want to evaluate your LLM outputs against. For instance, if you're building a medical information extraction system, your test dataset might include patient notes paired with the correct medical conditions that should be extracted from them.

2/ Evaluation Metrics

These are quantitative measures for evaluating different aspects of model performance. The best frameworks have evolved beyond simple accuracy metrics to address the complexities of LLM capabilities.

Good evaluation frameworks measure various dimensions, and offer you the capability to code custom metrics like factual correctness, relevance, coherence, safety efficiency.

3/ Scalable Infrastructure

Your evaluation framework should be fast and scalable, allowing you to run tens of thousands of test examples, with detailed reporting and visualization capabilities. The framework should also be able to work with user feedback, and route it into the evaluation table for further testing.

Relevant questions for scalability:

→ Can it manage your expected number of test cases? As your test suite grows, you need a framework that can handle thousands or even millions of evaluations efficiently.

→ Does it offer necessary security and compliance features? This is especially important if you're working with sensitive data or in regulated industries.

4/ Support for all kinds of evals

If you’re building for production you want your evaluation framework to support multiple evaluation approaches:

Offline evaluation: Pre-deployment testing against curated datasets. You run these as you prototype to catch issues before they reach users.

Online evaluation: Continuous monitoring of live systems in production. This helps you understand how your system performs with real user inputs and catch any drift in performance over time.

Inline evaluation: Runtime guardrails that prevent problematic outputs. These check every step of your workflow and evaluate if the output is correct before it reaches the user.

Relevant questions for types of evals:

→ Are you looking for offline testing during development? Online monitoring in production?

→ Do you only require inline guardrails to prevent bad outputs?

The best frameworks offer all three in an integrated system, and if you’re serious about your product, you’ll want to have all three.

5/ End to end evaluation

Finally, the most powerful frameworks will allow you to test end-to-end across your whole backend, not just isolated prompts or components. This will become increasingly important as you develop more complex workflows.

Relevant questions for end-to-end testing:

→ How well does it integrate with your existing development tools and processes? This is where great evaluation frameworks can really shine.

→  Can evaluations be incorporated into automated testing pipelines? This is crucial for catching regressions before they reach production.

→ Can it seamlessly transition from development to production evaluation? It's best to have one system that can do both.

→ Can you test your workflow end-to-end and not only isolated prompts? Many frameworks only evaluate individual prompts, but real-world AI systems are complex pipelines with multiple components that interact with each other.

The right evaluation framework will check most of these boxes, but you'll likely need to prioritize based on your specific needs. For most teams building production AI systems, end-to-end testing capabilities and workflow integration should be at the top of the list, areas where Vellum has established itself as the industry leader.

Evaluate with Vellum

Most evaluation tools stop at the prompt, but real-world systems are way more complex. Vellum lets you go deeper, testing full backend workflows, not just isolated components. Whether you're running offline evals, gathering online feedback, or adding inline checks before things go live, Vellum supports it all out of the box.

You can write your own custom metrics in Python or TypeScript, collaborate across teams with shared projects and reports, and tap into a growing library of prompt strategies, and eval setups.

It runs how you need it to, SaaS, VPC, or fully on-prem, and meets strict compliance standards like SOC2 Type 2 and HIPAA (with BAAs), with role-based access built in.

If you want to see a demo, book a call here.