Evaluating LLMs on Amazon Bedrock: A Practical Guide for finding the relevant LLM for your use case

When deploying large language models (LLMs) in real-world workflows, systematic evaluation is essential—especially for mission-critical tasks like automating personalized sales outreach. At CloudKeeper, our goal was to help Sales Development Representatives (SDRs) draft hyper-personalized emails using contact and company data, driving productivity gains and higher engagement rates. Here’s how we approached model evaluation on Amazon Bedrock, from model selection through hands-on assessment and result interpretation.

Why Model Evaluation Matters for SDR Workflows

Personalized outreach at scale is a nuanced challenge for generative AI. SDRs need emails that are not only grammatically correct but also tailored, context-aware, and responsible. Picking the right model—and proving its performance on your unique task—is the difference between busywork and real business impact.  For example, Amazon Nova creates extremely long emails, Claude 3.5 Haiku is more straightforward and follows instructions precisely, while Llama 3.3-70b writes catchy subject lines that boost open rates.

1. Model Selection on Amazon Bedrock

Scope: We focused on text-generation models available within Amazon Bedrock, filtering for those suitable for real-time, context-heavy email writing.

Initial Candidates Included: ai21.jamba-1-5-mini-v1:0, amazon.nova-micro-v1:0, cohere.command-r-v1:0, mistral-small-2402-v1:0, anthropic.claude-3-5-haiku-20241022-v1:0, llama3.3-70b-instruct-v1

Note:  Access to advanced models such as Claude 3.5 and Llama 3-70B is limited to the playground or requires purchasing throughput.

Best Practice:

Start broad—benchmark all feasible models on your specific use case before narrowing the field. Don’t assume “bigger” is better; smaller models may be faster and more cost-effective if they meet your quality bar.

2. Amazon Bedrock Evaluation Workflows

Bedrock offers three robust evaluation pathways:

a. Automatic Model Evaluation Jobs

• What: Fast, repeatable model benchmarking using datasets (either custom or built-in) to assess basic task performance.
• When: Early-stage filtering or regression tests after model updates.

b. Human-in-the-Loop Evaluation Jobs

• What: Involve in-house reviewers or external experts to score and comment on outputs.
• When: For nuanced tasks, or when subjective human judgment is needed.

c. Judge-Model Evaluation Jobs (Our Choice)

• What: Use a separate LLM (“judge model”) to evaluate model responses, assigning both a score and an explanatory rationale.
• Why we picked it: We leveraged Claude 3.7 Sonnet as our judge model, valuing its strong reasoning, speed, and consistency over manual reviews.

RAG-Specific Evaluation

Bedrock’s LLM-based judge workflows extend to Retrieval-Augmented Generation (RAG), letting you evaluate not just LLM responses but also the relevance of retrieved content from knowledge bases or external data sources.

Best Practice:

Automate what you can, but periodically validate with human reviews—especially for high-stakes or evolving tasks.

3. Setup Steps: Data, Metrics, and Storage

a. Dataset Preparation

• Format: Store custom prompts (and, optionally, <referenceResponse> ground truths) in JSONL files.
• Location: Upload these files to an S3 bucket accessible by Bedrock.

Structure Example:

{"input": "Write a personalized email to Jane Doe at Acme Corp about our cloud savings platform.", "referenceResponse": "Hi Jane, I noticed Acme Corp recently expanded its AWS footprint..."}

b. Metric Configuration

• Quality: Bedrock provides built-in scoring for relevance, fluency, and completeness.
• Responsible AI: Out-of-the-box checks for toxicity, bias, etc.
• Custom Metrics: Define additional measures (e.g., personalization, factual accuracy) as needed.

c. Evaluation Output

Storage: Results (including scores and judge model explanations) are saved back to S3 and available in the Bedrock console.

Best Practice:

Align your evaluation dataset closely with real-world SDR prompts. Always include expected responses if possible; this helps both human and LLM judges evaluate meaningfully.

4. Interpreting Results and Using Bedrock’s Visualization Tools

After jobs run:

• Scores are normalized between 0 and 1, enabling easy comparison.

  

• Judge Explanations provide context for each score, highlighting strengths and areas for improvement.
• Visualization: The Bedrock console displays:

1. Job-over-job comparison charts.

2. Metric breakdowns (by prompt, by category).

   

Best Practice:

Use visualizations to identify patterns—are certain prompt types consistently underperforming? Are some models excelling at personalization but lacking in factual accuracy? Iterate your evaluation dataset and metrics to surface these insights.

Key Takeaways & References

• Systematic evaluation is critical for responsible, effective LLM deployment—especially in customer-facing roles like SDRs.
• Amazon Bedrock streamlines this process with multiple evaluation workflows, seamless S3 integration, and robust visualization.
• Judge-model evaluation (using a strong LLM like Claude 3.7 Sonnet) provides both quantitative and qualitative insight at scale.
• Actionable outputs—normalized scores, judge rationales, and clear visualizations—empower teams to choose the best model for their needs.

Further Reading:

• Amazon Bedrock Documentation – Model Evaluation
• Prompt Engineering for LLM Applications
• AI security on Amazon Bedrock

Deploying LLMs for high-stakes, real-world tasks demands more than intuition—let Amazon Bedrock’s evaluation tooling be your guide to consistent, measurable, and scalable success.