Building Intelligent Apps with Generative AI
Course Description
Learn to design, build, and ship real-world Generative AI applications. This program covers core LLM concepts, prompt engineering, Retrieval Augmented Generation (RAG), vector databases, model fine-tuning, and deploying AI apps using Python frameworks. Perfect for developers and data practitioners aiming to add AI capabilities to their products.
By the end of this course, you will have built production-ready AI features such as chatbots, content generators, and custom RAG systems integrated with your own data.
What you will learn in this course
- Prompt engineering patterns and best practices.
- LLM fundamentals: tokens, temperature, context windows.
- Build RAG pipelines with embeddings and vector stores.
- Fine-tune and evaluate models for specific tasks.
- Create chatbots and content generators with Python.
- Secure and deploy AI apps (APIs, UI integration).
Detailed Curriculum
- Module 1: Generative AI landscape, LLMs, and safety.
- Module 2: Prompt engineering strategies and evaluation.
- Module 3: Embeddings, vector databases, and similarity search.
- Module 4: Retrieval Augmented Generation (RAG) end-to-end.
- Module 5: Fine-tuning techniques and model evaluation.
- Module 6: Building chatbots and content tools in Python.
- Module 7: Deploying and securing AI apps (APIs, UI).
- Module 8: Capstone project and presentation.
Tools & Technologies
Python, LangChain/LlamaIndex, OpenAI or compatible LLM APIs, Hugging Face, FAISS/Chroma, Flask/FastAPI, Streamlit, Git & GitHub, Docker (optional).
Hands-on Projects
- RAG QA assistant over your PDFs/website.
- Content generation tool (blogs, ads, emails) with prompt templates.
- Domain chatbot with memory and guardrails.
Who Is This For?
Developers, data engineers, and tech professionals who want to build real AI product features.
Prerequisites
- Basic Python and APIs.
- Familiarity with Git and JSON.
- Optional: basic NLP knowledge.
Certification & Career Paths
Earn a course completion certificate. Roles: AI Engineer, LLM Application Developer, Prompt Engineer, ML Engineer (Applied), Solutions Engineer.
FAQs
- Do I need GPU access? No. We use hosted APIs and small local models when needed.
- Will I build a full project? Yes, a capstone RAG or chatbot app you can showcase.
- Is math-heavy ML required? No, we focus on applied LLM engineering.
- Do you cover deployment? Yes, packaging APIs/UI and basic security.