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
1) Start with the Reality: Why Observability Matters Building AI agents is exciting—but running them in production is where most challenges appear. Common problems include: This is why observability is essential. Observability allows you to understand exactly what your AI agent is doing at every step—from input to final output. Platforms like LangSmith, developed by