AI Crash Course for Beginners

Confused about AI, Generative AI, LLMs, RAG, AI Agents and MCP? This crash course is designed to take you from AI fundamentals to modern AI application development without assuming advanced mathematics or prior AI experience.

You’ll learn how modern AI systems work, how LLM applications are built, and how different components such as prompts, embeddings, vector search, RAG, tools and agents fit together.

What You'll Learn

AI & GenAI Fundamentals

• AI vs Machine Learning vs Deep Learning vs Generative AI
• How Machine Learning works
• Neural networks and foundation models
• How LLMs work
• Tokens, context windows and inference
• Transformers and attention

Prompt Engineering

• How to write effective prompts
• Role, Task, Context, Constraints and Output format
• Improving and evaluating AI responses
• Techniques for making AI outputs more reliable

RAG & Vector Search

• Embeddings and semantic search
• Vector databases and vector search concepts
• How RAG works end-to-end
• Document chunking and retrieval
• RAG vs Fine-Tuning
• Building a simple RAG application
• Production considerations such as hybrid retrieval, metadata filters, reranking, citations and evaluation

AI Agents

• What makes an AI system agentic
• Agent architecture and the Perceive → Reason → Act → Observe loop
• Tool calling and function calling
• ReAct pattern
• Agent memory
• Planning strategies
• Reflection and self-correction
• Building tool-using AI workflows

MCP — Model Context Protocol

• What MCP is and why it matters
• MCP Host, Client and Server architecture
• Tools, Resources and Prompts
• MCP transports and capability discovery
• Connecting agents with external tools and data
• Building a basic MCP server
• Understanding multi-server agent architectures
• MCP security, permissions and best practices

Building AI Applications

You’ll understand how models, application logic, retrieval, databases, tools, APIs, guardrails and evaluation come together to create real AI applications.

Who Is This For?

This is ideal for:

• Beginners starting their AI journey
• Data Analysts and Data Engineers moving into AI
• Software professionals exploring GenAI
• Students and working professionals
• Anyone who wants to understand modern AI without getting overwhelmed by advanced mathematics

What You’ll Walk Away With

By the end, you should be able to explain the modern AI landscape, understand how LLM applications work, write better prompts, understand RAG and agents, read basic AI code, and identify what to learn next.

You’ll also have a clear foundation for moving toward AI Application Development, AI Automation, Agentic AI, RAG-based applications or AI Engineering.

The goal is simple:

Learn → Experiment → Build → Verify → Iterate