Generative AI Application Development: From Prompts to Production

This course teaches you how to transform large language models into useful, dependable applications. It is designed for developers, AI learners, product builders, and professionals who want to move beyond basic chatbot demonstrations and create applications that solve meaningful business problems.

You will first understand how generative models and large language models work at an application level. The course covers tokens, context windows, model parameters, inference behaviour, temperature, prompt structure, system instructions, few-shot learning, and structured responses.

You will then build applications that communicate with language models through APIs. Practical lessons cover conversation management, prompt templates, response validation, JSON outputs, function calling, external tools, document processing, streaming responses, and multimodal inputs.

The class introduces AI-agent concepts such as goals, tools, planning, state, memory, and controlled execution. You will learn when an agent is useful—and when a simpler deterministic workflow is safer, faster, and more economical.

Reliability is treated as a central engineering requirement. You will evaluate output quality, reduce hallucinations, manage context, design fallbacks, handle API failures, moderate unsafe content, protect sensitive information, and monitor token usage and cost.

Projects may include an intelligent assistant, document-processing application, business workflow copilot, content-generation system, or domain-specific AI tool. Each project emphasises modular architecture and practical user experience.

By the end of the class, you will be capable of designing, building, evaluating, and preparing a generative-AI application for deployment while making informed decisions about models, prompts, tools, safety, latency, and cost.