Getting Started with MLflow for Large Language Model (LLM) Evaluation: A Step-by-Step Guide for Data Scientists

If you’re experimenting with Large Language Models (LLMs) like Google’s Gemini and want reliable, transparent evaluation—this guide is for you. Evaluating LLM outputs can be surprisingly tricky, especially as their capabilities expand and their use cases multiply. How do you know if an LLM is accurate, consistent, or even safe in its responses? And how do you systematically track and compare results across experiments so you can confidently improve your models?

That’s where MLflow steps in. Traditionally known for experiment tracking and model management, MLflow is rapidly evolving into a robust platform for LLM evaluation. The latest enhancements make it easier than ever to benchmark LLMs using standardized, automated metrics—no more cobbling together manual scripts or spreadsheets.

In this hands-on tutorial, I’ll walk you through evaluating the Gemini model with MLflow, using a set of fact-based prompts and metrics that matter. By the end, you’ll know not just how to run an LLM evaluation workflow, but why each step matters—and how to use your findings to iterate smarter.

Let’s dive in.

Why You Should Care About LLM Evaluation (And What’s New)

You might wonder, “Don’t LLMs just work out of the box?” While today’s models are impressively capable, they’re not infallible. They can hallucinate facts, misunderstand context, or simply give inconsistent answers. If you’re deploying LLMs in production—for search, chatbots, summarization, or anything mission-critical—evaluation isn’t optional. It’s essential.

Here’s why:

Measuring progress: Objective metrics illuminate where your model shines or struggles.
Comparing models: Standardized evaluation lets you benchmark Gemini vs. GPT-4 or any custom LLM.
Building trust: Reliable outputs lead to user confidence—and stakeholder buy-in.
Streamlining iteration: Automated tracking helps you focus on improvements, not busywork.

MLflow’s recent updates add out-of-the-box support for evaluating LLMs—leveraging the strengths of both OpenAI’s robust metrics and Gemini’s powerful generation capabilities.

Prerequisites: What You’ll Need Before You Start

Before we jump into code, let’s set the stage:

1. Access & API Keys

You’ll need:

OpenAI API Key: MLflow’s generative AI metrics use OpenAI’s GPT models to score LLM outputs (e.g., for semantic similarity). Get your key here.
Google Gemini API Key: Needed to generate predictions from Gemini. Sign up and obtain your key here.

Tip: Never hard-code your API keys in scripts or notebooks. Use environment variables for security.

2. Python Environment & Libraries

Make sure you have the following libraries installed:

shell pip install mlflow openai pandas google-genai

mlflow: For tracking experiments and evaluation metrics.
openai: To access OpenAI models for metrics.
google-genai: The official SDK for Gemini.
pandas: For data handling.

Step 1: Setting Up Your Environment

Let’s kick things off by securing your API keys so you can safely use both Gemini and GPT-powered evaluation metrics.

“`python import os from getpass import getpass

os.environ[“OPENAI_API_KEY”] = getpass(‘Enter OpenAI API Key:’) os.environ[“GOOGLE_API_KEY”] = getpass(‘Enter Google API Key:’) “`

Why do this? Environment variables keep your sensitive credentials out of code that might get shared or version-controlled. Always a good habit!

Step 2: Preparing Your Evaluation Data

A well-chosen evaluation dataset is the backbone of any successful LLM testing workflow. For this guide, we’re focusing on fact-based prompts—a common use case for LLMs in question-answering, support chatbots, and educational tools.

Here’s a sample of what our dataset looks like:

“`python import pandas as pd

eval_data = pd.DataFrame( { “inputs”: [ “Who developed the theory of general relativity?”, “What are the primary functions of the liver in the human body?”, “Explain what HTTP status code 404 means.”, “What is the boiling point of water at sea level in Celsius?”, “Name the largest planet in our solar system.”, “What programming language is primarily used for developing iOS apps?”, ], “ground_truth”: [ “Albert Einstein developed the theory of general relativity.”, “The liver helps in detoxification, protein synthesis, and production of biochemicals necessary for digestion.”, “HTTP 404 means ‘Not Found’ — the server can’t find the requested resource.”, “The boiling point of water at sea level is 100 degrees Celsius.”, “Jupiter is the largest planet in our solar system.”, “Swift is the primary programming language used for iOS app development.” ] } ) “`

Why fact-based prompts?
They’re easy to verify, make metric comparison more objective, and closely reflect many real-world LLM applications.

Pro Tip:
If you have your own use case, customize the prompts and ground truth answers accordingly. Just ensure the answers are unambiguous.

Step 3: Generating Predictions with Gemini

Now let’s get Gemini to generate responses for each prompt.

First, set up the Gemini SDK client:

“`python from google import genai

client = genai.Client() “`

Now, create a function for querying the Gemini 1.5 Flash model:

python def gemini_completion(prompt: str) -> str: response = client.models.generate_content( model="gemini-1.5-flash", contents=prompt ) return response.text.strip()

Apply this function to each prompt and store the results:

python eval_data["predictions"] = eval_data["inputs"].apply(gemini_completion)

Here’s why that matters:
By storing predictions alongside inputs and ground truth, you make downstream evaluation, reporting, and troubleshooting much easier.

Step 4: Evaluating LLM Outputs with MLflow

Now for the fun part: using MLflow to measure how well Gemini performed. Instead of cobbling together separate scripts for each metric, you can use mlflow.evaluate() to run a comprehensive evaluation suite in one go.

Setting the Experiment

First, tell MLflow where to track results:

“`python import mlflow

mlflow.set_tracking_uri(“mlruns”) mlflow.set_experiment(“Gemini Simple Metrics Eval”) “`

Evaluating with Key Metrics

Start a new MLflow run and evaluate your results:

python with mlflow.start_run(): results = mlflow.evaluate( model_type="question-answering", data=eval_data, predictions="predictions", targets="ground_truth", extra_metrics=[ mlflow.metrics.genai.answer_similarity(), mlflow.metrics.exact_match(), mlflow.metrics.latency(), mlflow.metrics.token_count() ] ) print("Aggregated Metrics:") print(results.metrics) results.tables["eval_results_table"].to_csv("gemini_eval_results.csv", index=False)

What do these metrics mean?

answer_similarity: Measures semantic closeness using GPT as a “judge” (ideal for paraphrased but correct answers).
exact_match: Did the prediction match the ground truth word-for-word?
latency: How quickly did Gemini generate each answer?
token_count: How verbose were the answers?

Important:
MLflow’s answer_similarity metric relies on the OpenAI API. This cross-evaluation is a best-of-both-worlds approach—Gemini for generation, OpenAI for scoring.

Step 5: Inspecting and Analyzing the Results

After running your evaluation, you’ll want to review the detailed results. Load the CSV you just saved:

“`python import pandas as pd

results = pd.read_csv(‘gemini_eval_results.csv’) pd.set_option(‘display.max_colwidth’, None) results “`

Now you can visually inspect:

The prompt (“inputs”)
Gemini’s answer (“predictions”)
The correct answer (“ground_truth”)
Each metric score for every example

This is invaluable for spotting patterns—maybe Gemini struggles with programming prompts but excels at scientific facts. Use these insights to fine-tune your prompts, adjust model parameters, or even retrain models if needed.

Interpreting the Metrics: What Do the Numbers Actually Mean?

Numbers are only useful if you know what to do with them. Here’s how to think about each key metric:

High answer_similarity, low exact_match: Gemini gives correct answers but not verbatim. This is often a good thing—models shouldn’t just parrot the ground truth.
Low answer_similarity: Indicates a gap in factual accuracy or understanding. Investigate prompts and model behavior.
High latency: May signal response bottlenecks; monitor if you’re building time-sensitive applications.
Token count: Longer isn’t always better. Assess for unnecessary verbosity, which can confuse users or drive up API costs.

Actionable tip:
Sort your results by answer_similarity or exact_match to quickly find weak spots or failure cases.

Why Use MLflow for LLM Evaluation?

You might be asking, “Couldn’t I just use spreadsheets or notebooks?” Sure—but here’s why MLflow is a game-changer for LLM evaluation:

Automated experiment tracking: No more manual record-keeping or risk of forgetting your best runs.
Reproducibility: MLflow logs parameters, metrics, and artifacts for every experiment—critical for collaborations and audits.
Scalability: Evaluate hundreds or thousands of prompts efficiently.
Visualization tools: MLflow UI (run mlflow ui in your terminal) lets you visualize trends, compare runs, and share results with your team.

Further reading:
For more on MLflow’s core capabilities, check out the official MLflow documentation.

Limitations and Gotchas

No tool is perfect! Here are a few things to keep in mind:

API costs: Both OpenAI and Gemini APIs can incur costs at scale. Monitor usage carefully.
Evaluation bias: Using a different model (GPT) to judge Gemini can introduce bias. For deeper assessments, consider human review or multiple evaluators.
Prompt quality: Results are only as good as the prompts and ground truths you provide. Garbage in, garbage out.
Latency variance: Network conditions and API load may affect latency scores.

Next Steps: Taking Your LLM Evaluation Further

Congrats—you’ve now built a reproducible, scalable workflow for evaluating LLM outputs with MLflow! Here’s how you can take your project to the next level:

Expand your dataset: Test with more diverse or domain-specific prompts.
Add custom metrics: Leverage MLflow’s extensible API to track advanced metrics like factual consistency, toxicity, or bias.
Compare models: Run head-to-head evaluations between Gemini, GPT-4, and any fine-tuned models you’re developing.
Automate runs: Integrate this workflow into ML pipelines or CI/CD systems for continuous quality monitoring.

If you’re curious about advanced topics—like evaluating multi-turn conversations, or integrating human-in-the-loop feedback—check out recent research and best practices from Hugging Face and the MLflow blog.

Frequently Asked Questions (FAQ)

1. Can I use MLflow to evaluate any LLM, or just Gemini and OpenAI models?
Yes, MLflow’s generative AI evaluation is model-agnostic. You can use it to evaluate outputs from any LLM, as long as you provide the generated predictions and the ground truth answers.

2. Do I need both OpenAI and Gemini API keys?
For this workflow, yes. Gemini is used for generating answers; OpenAI’s API powers the built-in evaluation metrics (like answer_similarity). If you use only Gemini, you’ll need to implement custom evaluation logic.

3. Are my API keys safe when using environment variables?
Safer than hard-coding, but always follow best practices: never expose keys in code or output files, and consider using tools like AWS Secrets Manager or HashiCorp Vault for production workloads.

4. How do I visualize MLflow experiments?
Simply run mlflow ui in your terminal. This opens a local MLflow tracking UI in your browser, allowing you to compare runs, plot metrics, and review artifacts.

5. What if I want to evaluate non-factual or creative prompts?
MLflow supports custom metrics! You can extend its evaluation pipeline to suit your task—summarization, code generation, chatbots, and more.

6. Can I use MLflow with other languages besides Python?
MLflow’s core tracking features support Java, R, and REST APIs as well. However, generative AI and LLM evaluation features are most mature in Python.

7. Is there a risk of model bias when using GPT to judge Gemini outputs?
Potentially. Automated metrics offer scalability, but for critical use cases, supplement with human review or ensemble evaluations.

Key Takeaways & Next Steps

Evaluating LLMs doesn’t have to be a black box or a manual slog. By leveraging MLflow’s generative AI metrics, you can:

Benchmark LLMs like Gemini on fact-based tasks
Automate and standardize evaluation
Gain actionable insights to guide model improvements

Whether you’re a data scientist deploying chatbots, a developer curious about LLM reliability, or a machine learning engineer building evaluation pipelines, this workflow can save you hours and boost your confidence.

Ready to go deeper?
Check out the MLflow LLM evaluation documentation or subscribe for more practical, hands-on guides to AI development.

Happy evaluating! Have questions or want to share your own results? Leave a comment or connect with me on LinkedIn—I’d love to hear how you’re using MLflow for LLMs.

If you found this tutorial helpful, be sure to bookmark it or share it with your team. The AI space moves fast—stay curious and keep experimenting!

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Getting Started with MLflow for Large Language Model (LLM) Evaluation: A Step-by-Step Guide for Data Scientists

Why You Should Care About LLM Evaluation (And What’s New)