🟦 Generative AI for Developers Course
This comprehensive Generative AI for Developers course is designed to equip software engineers and aspiring AI developers with the practical skills needed to build modern AI-powered applications. It focuses on hands-on development using large language models (LLMs), AI frameworks, and vector database technologies.
The course begins with core concepts of Generative AI and how large language models such as GPT, Gemini, and Llama process and generate human-like text. Developers will learn how these models work under the hood, including tokenization, embeddings, and transformer architectures.
Next, the course introduces prompt engineering techniques to effectively communicate with AI models and optimize responses for different use cases. Learners will also explore Retrieval-Augmented Generation (RAG), which allows AI systems to retrieve external knowledge and produce more accurate, context-aware outputs.
A major part of the course focuses on building AI applications using tools like LangChain and integrating vector databases such as Pinecone or similar systems for semantic search and memory. Students will also learn how to design and deploy AI agents and real-world applications like chatbots, document assistants, and automation tools.
By the end of the course, learners will be able to confidently design, build, and deploy scalable Generative AI applications using industry-standard tools and workflows.
🟨 1. Foundations of Generative AI
This section introduces the core concepts behind Generative AI and modern LLMs.
🟩 1.1 What is Generative AI?
Understand the basic idea of Generative AI and how it creates human-like outputs such as text, images, and code.
🟩 1.2 How Large Language Models Work
Learn how models like GPT, Gemini, and Llama process language and generate responses.
🟩 1.3 Transformer Architecture Basics
Explore the architecture that powers modern AI language models.
🟩 1.4 Tokenization and Embeddings
Understand how text is converted into numerical representations for AI processing.
🟨 2. Prompt Engineering for Developers
This section focuses on how to effectively interact with AI models.
🟩 2.1 Basics of Prompt Engineering
Learn how to write clear prompts to get accurate AI responses.
🟩 2.2 Advanced Prompting Techniques
Explore structured prompts, role prompting, and contextual instructions.
🟩 2.3 Optimizing AI Outputs
Improve response quality by refining prompts and input structure.
🟨 3. Retrieval-Augmented Generation (RAG)
This section introduces how AI systems use external knowledge sources.
🟩 3.1 What is RAG?
Understand how retrieval enhances AI accuracy.
🟩 3.2 RAG Architecture
Learn how retrieval systems connect with language models.
🟩 3.3 Real-World Use Cases of RAG
Explore applications like document search and intelligent assistants.
🟨 4. Building AI Applications
This section focuses on practical development using modern AI tools.
🟩 4.1 LangChain Fundamentals
Learn how to build AI workflows using LangChain.
🟩 4.2 Vector Databases (Pinecone & Others)
Understand how semantic search and memory systems work.
🟩 4.3 AI Agents Development
Build intelligent agents that can perform tasks autonomously.
🟩 4.4 Real-World AI Applications
Create chatbots, document assistants, and automation tools.
🟨 5. Deployment & Production
This section covers how to deploy AI applications in real environments.
🟩 5.1 Deploying AI Applications
Learn how to launch AI apps for real users.
🟩 5.2 Scalability and Performance
Understand how to optimize AI systems for production usage.
🟩 5.3 Production Best Practices
Learn industry standards for building reliable AI systems.
🟨 6. Final Outcomes
By the end of this course, learners will be able to design, build, and deploy scalable Generative AI applications using modern tools like LLMs, LangChain, vector databases, and RAG systems.
They will have strong practical skills to develop real-world AI products and production-ready systems.
