1) Opening Scenario: From Idea to Agent Imagine you’re tasked with building an internal AI assistant for a mid-sized enterprise. The requirements are clear but demanding: Traditionally, this would require stitching together multiple services: a vector database for retrieval, an LLM for reasoning, API integrations for tools, and a deployment infrastructure. Months of work, complex
Introduction You have built an AI model. It is trained, validated, and ready for deployment. But how do you know if it is actually any good? Measuring AI model performance is not as simple as looking at a single number. A model that is 95% accurate on a dataset might be completely useless for your
Introduction Building an AI system is not a one-step process. It is not just about training a model and putting it into production. Successful AI requires a disciplined journey—from understanding the problem, to gathering and preparing data, to building and validating models, to deploying and monitoring them in the real world. This journey is called