Understanding LLM Benchmarks

Welcome back to The Evaluator—curated intel for anyone shipping, scaling, or debugging real-world AI systems. This issue is stacked: we're breaking down the latest in LLM benchmarks, showcasing new ways to build observable AI agents, and surfacing the AI research and events that matter.

Here’s what’s inside:

• A deep dive into LLM benchmarks
• New approaches to building & deploying observable agents
• Can’t-miss AI events and fresh research drops

P.S. We're giving away swag to celebrate Phoenix hitting 5K stars on GitHub—Phoenix wouldn’t be the same without you! Claim your free swag here.

With the accelerated development of GenAI, there is a particular focus on testing and evaluation, resulting in the release of many LLM benchmarks. Each of these benchmarks tests the LLM’s different capabilities–but are they sufficient for a complete real-world performance evaluation?

This blog covers some of the most popular LLM benchmarks for evaluating top models like GPT 4o, Gemma 3, or Claude. We also discuss LLMs’ use in practical scenarios and whether these benchmarks are sufficient for complex implementations like agentic systems. Read it here.

This technical guide explores the newly announced integration between Arize AI and Amazon Bedrock Agents, which provides developers with powerful capabilities for tracing, evaluating, and monitoring AI agent applications.

The integration between Arize AI and Amazon Bedrock Agents delivers three primary benefits:

1. Comprehensive Traceability: Gain visibility into every step of your agent’s execution path, from initial user query through knowledge retrieval and action execution
2. Systematic Evaluation Framework: Apply consistent evaluation methodologies to measure and understand agent performance
3. Data-Driven Optimization: Run structured experiments to compare different agent configurations and identify optimal settings

Read more here.

By combining Gemini’s audio transcription prowess with Arize AX’s OpenTelemetry-based tracing infrastructure and the Phoenix evaluation framework, developers can gain unprecedented visibility into their audio processing pipelines.

This blog walks through implementing a complete workflow that not only generates high-quality transcriptions, but also traces each step of the process and evaluates Gemini’s transcription outputs for sentiment analysis, allowing teams to identify issues, measure quality, and continuously improve their audio-based AI applications. Dive in here.

More Useful Guides & Updates

🧑🚀 LibreEval: The Largest RAG Hallucination Dataset

📚 AI Research: Keep up with the Latest 

📊 Learn Something: AI Agents & Assistants Handbook

🛠️ New In Arize: Bigger Datasets

🕵️ Explore More: Go to Arize Documentation

Upcoming Events

There’s a lot to talk about right now—let’s get you in the room with others who are building. Here’s where to find Arize at upcoming events:

• May 7, Virtual | Evaluating AI Agents Series, Part 1
• May 10, Seattle | Open Source AI Hackathon 
• May 16–18, Palo Alto | Agentic Startup RAG-a-thon
• May 20, San Francisco | AI Product Managers Meetup

JUNE 25: ARIZE OBSERVE

Be a part of the conversation about how we evaluate and deploy the next generation of AI systems. Join us June 25 at Shack 15 on the Embarcadero in San Francisco. Speakers from Google, OpenAI, NVIDIA, AWS, Anyscale, Mem0, and more. GET TICKETS.

 AI News & Papers

Here are some of the biggest ideas, breakthroughs, and debates that sparked discussions in our Slack this month.

• Anthropic's AI for Science Program
• Sleep-Time Compute: Beyond Inference Scaling at Test-time
• New Dataset Designed to Help Spot Harmful Stereotypes
• HuggingFace Agents Course
• The Definitive Guide to LLM Evaluation

Build. Learn. Connect.

Whether you're deep in the trenches building AI agents or just exploring what's next, we've got the resources to help:

Request a Demo | Arize Community Slack