LLM ENGINEERING AND GENERATIVE-AI

Categories: Machine Learning
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About Course

Large language models and generative AI have become the backbone of modern AI applications, and this course takes you from foundational concepts to building and deploying real generative AI systems. You will work hands-on with both closed-source APIs and open-source models running locally with Ollama, build retrieval-augmented generation pipelines, fine-tune models with LoRA and QLoRA, and design AI agents capable of multi-step reasoning.

Every lesson is grounded in practical, real-world workflows: you will build a RAG system, fine-tune an LLM, create tool-calling agents, and ship a complete generative AI application, not just study the theory behind it. By the end, you will have the practical LLM engineering skills needed to build production-grade generative AI systems.

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What Will You Learn?

  • Understand how large language models work, from tokenization to attention and generation
  • Write effective prompts and design reliable instructions for LLM applications
  • Work with both closed-source LLM APIs and open-source models running locally with Ollama
  • Build retrieval-augmented generation (RAG) systems, from basic pipelines to advanced retrieval techniques
  • Use embeddings and vector databases to power search and retrieval
  • Fine-tune large language models efficiently using LoRA and QLoRA
  • Build AI agents that call tools and reason through multi-step tasks
  • Design multi-agent systems using agent orchestration frameworks
  • Evaluate, test, and deploy LLM applications for real-world use
  • Build a complete, end-to-end generative AI application

Course Content

Foundations of LLMs and Generative AI

  • Introduction to Large Language Models and Generative AI Systems
  • Prompt Engineering and Instruction Design
  • Working with LLM APIs and Running Open-Source Models Locally with Ollama
  • Foundations of LLMs and Generative AI Quiz

Retrieval and Knowledge Systems

Fine-Tuning and Customization

AI Agents and Reasoning Systems

Evaluation, Deployment, and Production

Capstone

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