How to Setup LLM Evaluations Easily: A Step-by-Step Guide to RAG Model Testing with Amazon Bedrock

In today’s rapidly evolving AI landscape, ensuring the accuracy and reliability of your language models is not just important—it’s essential. Whether you’re running a business chatbot or deploying AI-powered tools at scale, the mantra remains true: “If you can’t measure it, you can’t improve it.” This comprehensive guide, inspired by expert AI developer Matthew Berman, walks you through setting up Large Language Model (LLM) evaluations, specifically Retrieval-Augmented Generation (RAG) evaluations, using Amazon Bedrock. You’ll learn how to benchmark your models effectively, create knowledge bases, and analyze results to continuously improve your AI implementations.

Table of Contents

• 🔍 Understanding the Importance of Model Evaluations
• 🏨 The Hotel Use Case: A Real-World Example
• 👤 Creating Your First AWS IAM User for Evaluations
• 📁 Setting Up Your AWS S3 Buckets: Organizing Context, Prompts, and Evaluations
• 📝 Preparing Your Prompts: The Foundation of Meaningful Evaluations
• 📚 Creating and Syncing Your Knowledge Base in Amazon Bedrock
• ⚙️ Setting Up RAG Evaluations: Testing Your Model’s Retrieval and Answering Abilities
• 📊 Reviewing and Interpreting Your Evaluation Results
• ⚔️ Comparing Multiple Models: Finding the Best Fit for Your Use Case
• 🎯 Custom Metrics: Tailoring Evaluations to Your Unique Requirements
• 💡 Best Practices for Effective LLM Evaluations
• 🔗 Wrapping Up: Bringing It All Together
• ❓ Frequently Asked Questions (FAQ)

🔍 Understanding the Importance of Model Evaluations

Imagine you have a chatbot serving customers for your hotel business. This chatbot relies on complex policy documents and terms of service to answer queries accurately. If it provides incorrect or misleading information, the fallout can be significant—ranging from poor customer experience to legal complications.

Model evaluations serve as the compass guiding your AI’s journey. They provide you with measurable metrics to determine whether your AI stack is improving or regressing as you iterate. Without these benchmarks, you’re flying blind, unable to confidently deploy or scale your AI solutions.

Amazon Bedrock offers a fully managed environment with access to some of the best AI models on the market—including giants like Meta and Anthropic, alongside Amazon’s proprietary models. It also includes powerful features like guardrails for safety, prompt routing, agents, and prompt management, making it ideal for production-grade AI implementations.

🏨 The Hotel Use Case: A Real-World Example

To make this tutorial concrete and practical, let’s consider a real-world scenario: you are a hotel owner with a twenty-six-page policy document containing legal terms, service policies, and operational details. Your goal is to build a chatbot that can answer potential visitors’ questions accurately by referencing this document.

This scenario highlights the complexity of working with dense, legalistic text and the need for a robust evaluation framework to ensure your chatbot responds correctly and helpfully.

👤 Creating Your First AWS IAM User for Evaluations

Before diving into the technical setup, you need appropriate AWS permissions. When you sign up for AWS, you start as a root user, but for security and operational reasons, you’ll want to create an IAM (Identity and Access Management) user to handle your evaluations.

Here’s a quick rundown of the process:

1. Navigate to the IAM service via the AWS console search bar.
2. Click on “Users” on the left panel and then “Create user.”
3. Enter a username (e.g., “Alex”).
4. Assign the user to a group with the necessary permissions, such as “Administrator Access.”
5. Enable console access by setting a password (auto-generated or custom) for the user.
6. Save the credentials and use the provided sign-in URL to log in as this new user.

This IAM user will be your operational identity for managing S3 buckets, knowledge bases, and evaluation jobs within Amazon Bedrock.

📁 Setting Up Your AWS S3 Buckets: Organizing Context, Prompts, and Evaluations

To perform effective RAG evaluations, you need three core components:

• Context Document: The knowledge source your model will query, such as your hotel policy document.
• Prompts: A test set containing questions and expected answers (ground truth) to benchmark the model.
• Evaluation Storage: A place to store the results of your model evaluations for review and analysis.

Amazon S3 (Simple Storage Service) is a perfect place to store these components. Here’s how to set up your buckets:

1. Uploading the Hotel Policy Document

1. Search for “S3” in the AWS console and open the service.
2. Create a new bucket named something like “hotel-policy.”
3. Use default settings for simplicity, including object ownership and permissions.
4. Upload your hotel policy document (e.g., a PDF file) by dragging and dropping it into the bucket.

By default, S3 buckets are private. To enable access by other AWS services (like Amazon Bedrock), you need to configure Cross-Origin Resource Sharing (CORS) settings:

1. Go to the bucket’s “Permissions” tab.
2. Scroll to the bottom to find “Cross-origin resource sharing (CORS).”
3. Edit the CORS configuration to include rules that allow access from other AWS services. (Sample configurations will be provided in your setup resources.)
4. Save the changes.

2. Creating Buckets for Prompts and Evaluations

Repeat the bucket creation process for:

• Prompts Bucket: Store JSONL files containing your test questions and ground truths.
• Evaluation Storage Bucket: Store the output of your evaluation jobs.

Remember to configure CORS permissions for these buckets as well, just like you did for the policy document bucket.

3. Organizing Your Evaluation Storage Bucket

Within your evaluation storage bucket, create a folder (e.g., “eval-store”) to keep your evaluation results neatly organized. This folder will serve as the destination for all your evaluation output files.

📝 Preparing Your Prompts: The Foundation of Meaningful Evaluations

Prompts are the questions and answers that your model will be tested against. They are essential for benchmarking because they provide the “ground truth” that your model’s responses will be compared to.

For example, a prompt might be:

Question: “I’ll be arriving late tomorrow night around 11 PM. What’s your check-in process, and will there still be someone at the desk to help me?”

Reference Response: “Yes, our front desk is staffed 24 hours to assist with check-in regardless of your arrival time.”

These prompts should be formatted in JSONL (JSON Lines) format for easy ingestion by the evaluation system. The format includes fields for the prompt text and the expected correct answer.

Once your prompt file is ready, upload it to the S3 bucket you created for prompts, ensuring it is easily accessible for the evaluation job.

📚 Creating and Syncing Your Knowledge Base in Amazon Bedrock

With your policy document uploaded, the next step is to create a knowledge base that your model can query. Amazon Bedrock allows you to create a vector store-based knowledge base from your documents.

Here’s how to create your knowledge base:

1. In Amazon Bedrock, select “Create knowledge base with vector store.”
2. Choose your S3 bucket containing the hotel policy document as the data source.
3. Keep the default settings or customize chunking strategies if needed.
4. Select an embeddings model—Amazon’s Titan Text Embeddings v2 is a great choice for text vectorization.
5. Review your settings and create the knowledge base.

Once created, it’s crucial to sync your knowledge base. Syncing prepares the data by converting it into vector format, making it ready for efficient querying by your language models. Without syncing, evaluations will not work.

You can also test your knowledge base by asking it simple questions using the built-in test feature, ensuring it’s correctly indexed and responding as expected.

⚙️ Setting Up RAG Evaluations: Testing Your Model’s Retrieval and Answering Abilities

Now comes the heart of the process—creating and running your RAG evaluations.

RAG evaluations test not only the model’s ability to retrieve the correct information from your knowledge base but also the quality of its generated responses based on that information.

Follow these steps:

1. Search for “Evaluations” in Amazon Bedrock and select the “RAG” tab.
2. Click “Create” to start a new evaluation job.
3. Name your evaluation and optionally add a description for easier management at scale.
4. Select an evaluator model, such as SONNET 3.7 v1. Keep in mind larger models offer better accuracy but take longer to run.
5. Choose your knowledge base from the dropdown menu.
6. Opt to test both retrieval and response generation instead of retrieval only.
7. Select an inference model for generating answers. For example, Amazon’s Nova Premier 1.0 offers a million-token context window—a powerful choice for complex documents.
8. Pick the metrics you want to evaluate. Common metrics include helpfulness, correctness, faithfulness, coherence, completeness, and relevance. Responsible AI metrics such as harmfulness, refusal, and stereotyping are also available.
9. Upload your prompt dataset from the S3 prompts bucket.
10. Specify the evaluation results storage folder in your evaluation S3 bucket.
11. Set permissions—create or select an existing service role that allows Bedrock to access your buckets.
12. Launch the evaluation job.

Depending on your dataset and model size, evaluations can take anywhere from a few minutes to an hour or more.

📊 Reviewing and Interpreting Your Evaluation Results

Once your evaluation job completes, you’ll have access to detailed metrics and example outputs that reveal how well your model performed.

For instance, in one evaluation:

• Helpfulness scores ranged from 0.67 to 0.83, indicating generally good responses.
• Correctness scores showed a majority of examples scoring between 0.9 and 1.0, with a few falling lower.

Each prompt’s output can be expanded to reveal:

• The model’s generated response.
• References to the exact text snippets from the knowledge base document used to generate the answer.
• The ground truth answer for comparison.
• A detailed explanation of the score, describing why the model received a particular rating. For example, a response might be praised for clarity, coherence, and detailed explanation but noted for recommending direct confirmation with the hotel for late check-ins.

This granular insight helps you understand not just whether a model is right or wrong, but how it reasons, communicates, and where it may need improvement.

⚔️ Comparing Multiple Models: Finding the Best Fit for Your Use Case

One of the most powerful features of Amazon Bedrock’s evaluation system is the ability to compare multiple models side-by-side.

Imagine you have two versions of a model—Nova Pro and Nova Premier—and you want to see which performs better on correctness and helpfulness.

By selecting two completed evaluations and clicking “Compare,” you get a detailed breakdown:

• Nova Pro might score slightly higher on correctness (e.g., 0.94 vs. 0.92).
• Nova Premier might edge out on helpfulness (e.g., 0.82 vs. 0.81).
• Visual distributions of scores let you see how consistent each model’s performance is across all prompts.
• Percentage improvements or decreases quantify the differences, helping you make data-driven decisions on which model to deploy.

🎯 Custom Metrics: Tailoring Evaluations to Your Unique Requirements

Beyond the standard metrics, Amazon Bedrock lets you define custom metrics to measure whatever qualities matter most to your use case.

For example, if you want your chatbot to speak like a pirate—saying “Arr, matey!” and adopting a nautical tone—you can create a “pirateness” metric. Your evaluation job would then score how well the model adheres to this style.

This flexibility opens up exciting possibilities for niche applications, branding consistency, or compliance requirements.

💡 Best Practices for Effective LLM Evaluations

• Start small and iterate: Begin with a manageable number of prompts and metrics, then expand as you gain confidence.
• Use realistic prompts: Your test questions should reflect actual user queries to get meaningful performance insights.
• Leverage multiple metrics: Don’t rely on correctness alone—consider helpfulness, coherence, and responsible AI factors.
• Regularly sync your knowledge base: Any updates to your documents require re-syncing to ensure evaluations use the latest data.
• Compare models frequently: Benchmark new models against your current best to track improvements or regressions.
• Document your evaluations: Use descriptions and consistent naming conventions for easier management.
• Consider evaluation runtime: Larger models provide nuanced results but take longer—balance speed and accuracy based on your needs.

🔗 Wrapping Up: Bringing It All Together

Setting up LLM evaluations might sound complex, but with tools like Amazon Bedrock and a clear process, it becomes manageable and invaluable. By following this step-by-step guide, you’ll gain the ability to:

• Organize your knowledge sources and prompts efficiently using AWS S3.
• Create vector-based knowledge bases to power retrieval-augmented generation.
• Launch sophisticated RAG evaluations that test retrieval and generation quality.
• Analyze detailed evaluation reports to understand your model’s strengths and weaknesses.
• Compare different models side-by-side to select the best fit for your production environment.
• Define custom metrics tailored to your unique business or branding needs.

Remember, model evaluation is not a one-time task but an ongoing practice that ensures your AI remains trustworthy, accurate, and helpful as it evolves.

❓ Frequently Asked Questions (FAQ)

What is RAG evaluation and why is it important?

RAG stands for Retrieval-Augmented Generation. It evaluates a model’s ability to retrieve relevant information from a knowledge base and generate accurate, helpful answers. This dual focus is crucial for applications like chatbots that rely on external documents to inform responses.

Can I use models from providers other than Amazon in these evaluations?

Yes! Amazon Bedrock supports models from multiple providers, including Meta, Anthropic, and others. You can also bring your own inference responses, allowing you to plug in external models and data sources.

How do I prepare my prompts for evaluation?

Prompts should be formatted as JSONL files containing the question and the ground truth answer. This format is easy for evaluation systems to process and compare model outputs against expected responses.

What are some common metrics used in evaluations?

Common metrics include helpfulness, correctness, faithfulness, coherence, completeness, and relevance. Responsible AI metrics like harmfulness, refusal, and stereotyping can also be included to ensure ethical AI behavior.

How long do evaluation jobs take to complete?

Evaluation runtimes vary based on the number of prompts, model size, and metrics selected. Jobs can take anywhere from a few minutes to over an hour. Larger models tend to take longer but provide more nuanced insights.

What is the benefit of creating custom metrics?

Custom metrics allow you to measure unique qualities or behaviors specific to your application. For example, you might want to enforce a particular tone, style, or compliance requirement, which standard metrics do not capture.

How often should I run evaluations?

Regular evaluations are recommended whenever you update your knowledge base, change models, or modify prompts. This ongoing practice helps track improvements or regressions and maintains AI quality over time.

Is it necessary to sync the knowledge base after updates?

Yes. Syncing converts updated documents into vector format, ensuring your evaluations query the most current information. Without syncing, your tests might use outdated or incomplete data.

Can I compare multiple evaluation results in Amazon Bedrock?

Absolutely. Bedrock provides tools to compare different evaluation runs side-by-side, helping you understand performance differences and choose the best model for your needs.

Do I need to be an AI expert to use Amazon Bedrock evaluations?

While some familiarity with AI concepts helps, Amazon Bedrock’s managed service and intuitive interface make it accessible to developers and business users who want to ensure their AI solutions perform well.

Where can I find resources and sample data to get started?

Amazon provides documentation, sample datasets, and configuration templates. Additionally, many AI practitioners share resources online to help you bootstrap your evaluation projects quickly.

By embracing model evaluations today, you set yourself up for AI success tomorrow. Start measuring, keep iterating, and watch your AI solutions improve in accuracy, helpfulness, and trustworthiness.