1) The Critical Challenge: Why Testing Autonomous Agents Is Different Imagine deploying an AI agent that handles customer support for your enterprise. It works perfectly in development—answering questions, escalating issues, processing refunds. Then, in production, it starts approving million-dollar refunds for anyone who asks. Or it gets stuck in infinite loops, calling APIs repeatedly until…
Introduction You have access to a powerful language model like ChatGPT, Claude, or Gemini. You type a question, and it answers. Sometimes the answer is perfect. Sometimes it is confusing, wrong, or useless. What makes the difference? Often, it is the prompt. Prompt engineering is the art and science of crafting effective instructions for AI language…
Introduction You have a large language model. It is powerful, but it does not know your specific domain. It was trained on public internet text—not your internal documents, not your product catalog, not your customer support history. You need it to understand your world. How do you make that happen? Two approaches dominate the conversation: fine-tuning and retrieval-augmented…