As large language models (LLMs) become foundational to enterprise AI, understanding how to evaluate them effectively is critical. But performance alone isn’t enough — organizations need a structured, transparent, and secure approach to assess how LLMs align with real-world business goals. 

IntelePeer’s whitepaper, “Best Practices for Evaluating LLMs,” offers a comprehensive framework for evaluating commercial LLMs in the context of agentic AI and analysis agents. 

This guide is designed to help enterprise leaders, AI strategists, and product teams make informed decisions about LLM adoption and deployment. 

Inside the whitepaper, you’ll explore: 

• The principles of agentic AI and how modular agents drive enterprise automation. 
• How analysis agents use LLMs to extract insights from customer interactions at scale. 
• A four-stage evaluation framework combining automation, human judgment, and privacy safeguards. 
• Comparative insights from testing GPT-4o, Claude 3, and Grok-3 in real-world CX scenarios.  

Whether you’re building AI-powered customer service workflows or exploring domain-specific applications, this whitepaper provides the strategic foundation to evaluate LLMs with confidence. 

Download “Best Practices for Evaluating LLMs” to deepen your understanding of how to responsibly and effectively integrate LLMs into enterprise systems.