LLM Foundational Course with Projects & AI Agent Development

The LLM Foundational Course is designed for developers, engineers, and builders who want to go beyond surface-level AI usage and truly understand how large language model systems work.

Unlike most courses that rely heavily on frameworks like LangChain or AutoGen, this course focuses on first-principles learning — meaning you will build everything directly using APIs. This approach ensures that you understand every layer of the system you create, from input tokens to final output.

You will begin by understanding how large language models process language, followed by a deep dive into critical concepts such as tokenization, context windows, and embeddings. These are not just theoretical ideas — you will implement them through real code and practical exercises.

As the course progresses, you will learn how to:

• Make structured API calls and interpret model responses

• Manage token costs efficiently across different use cases

• Handle limitations like context window size and memory constraints

• Build conversation memory for multi-turn AI interactions

• Implement semantic search using embeddings

• Develop Retrieval-Augmented Generation (RAG) pipelines

By the final stage, you will bring everything together to build a complete production-ready AI agent — capable of:

• Maintaining conversation memory

• Retrieving knowledge dynamically

• Generating accurate, context-aware responses

• Tracking token usage and cost in real time

This course is built entirely around a code-first approach, ensuring that every concept is backed by implementation — not just theory. 

What You Will Learn

• How large language models actually work behind the scenes

• Tokenization (Byte Pair Encoding) and its real impact on cost

• API fundamentals — request structure, response parsing, and limitations

• Context window management and token optimization strategies

• Embeddings and vector search for semantic understanding

• How cosine similarity enables intelligent information retrieval

• Controlling model behavior using temperature, max tokens, and stop sequences

• Designing production-ready AI systems with memory and retrieval

  
  

Tools & Technologies

• Python

• Claude API (Anthropic)

• Jupyter Notebook / Google Colab

• Vector Search (Custom Implementation)

• Embeddings & Cosine Similarity

• API-based AI Development (No Framework Dependency)

Who Should Enroll

• Python developers who want to build real AI-powered applications

• Engineers looking to understand LLMs beyond frameworks

• Freelancers building AI-based solutions for clients

• Startup founders developing AI products or SaaS platforms

• Developers working on chatbots, AI assistants, or knowledge systems

• Anyone interested in RAG, semantic search, or AI agent systems

Prerequisites

• Basic knowledge of Python (functions, loops, classes)

• Ability to run code in Jupyter Notebook or Google Colab

• Basic understanding of APIs (helpful but not mandatory)

• No prior AI/ML experience required