Generative AI Application Development: Prompting, Ollama, and LangChain

As artificial intelligence continues to reshape technology, the ability to build, customize, and deploy your own AI-powered solutions is becoming an essential skill. Whether you want to automate workflows, build smart chatbots, or keep your data private by running models locally, understanding the mechanics behind large language models is the key. This text-based course guides you through the process of developing functional AI applications from scratch. You will progress from understanding core generative AI concepts to crafting advanced prompts, running models on your own machine, and connecting them to external data sources. By studying clear explanations and practical code examples, you will gain the confidence to design intelligent systems that solve real-world problems. What you'll learn: - Understand the foundational concepts of generative AI, large language models, and key parameters like temperature and top-p. - Master prompt engineering techniques, including role prompting, few-shot learning, and chain-of-thought frameworks. - Configure and run open-source language models locally on your own hardware using Ollama. - Build dynamic, multi-step AI workflows and application pipelines using Python and the LangChain framework. - Implement Retrieval-Augmented Generation (RAG) to connect models to local documents and private data sources. - Create interactive user interfaces for your AI applications using Streamlit. The course starts with essential terminology and the mechanics of language models before moving step-by-step through prompt design, local deployment, and application development. You will read conceptual breakdowns and analyze structured code snippets designed to help you construct your own AI agents and workflows. This course is designed for developers, data analysts, tech enthusiasts, and beginners eager to enter the field of AI engineering. No prior experience with artificial intelligence is required, though a basic familiarity with Python programming will help you get the most out of the code examples. Start reading today to begin building your own local and cloud-based generative AI applications.