Traditional software testing doesn’t work for AI. As AI becomes embedded in enterprise applications, organizations are realizing that legacy testing methods fall short. From non-deterministic outputs to AI agents, AI systems require a new playbook.
This whitepaper discusses a comprehensive framework to help you test AI systems effectively.
In this whitepaper, you'll learn about:
Explore how organizations can ensure trustworthy, factually grounded responses in agentic RAG chatbots by evaluating contextual grounding methods, using Amazon Bedrock Guardrails and custom LLM-based scoring, to reduce hallucinations and build user confidence in high-stakes domains.
Discover what real-time Retrieval-Augmented Generation (RAG) is, how it works, the benefits and challenges of implementing it, and real-world use cases.
Learn how understanding tokenomics helps organizations optimize the cost and profitability of their generative AI applications—making them both financially sustainable and scalable.
