Skip to main content
AI has advanced rapidly over the past few years. Today’s practical toolkit for building intelligent applications includes concepts and technologies such as prompt engineering, context windows, tokens, embeddings, Retrieval-Augmented Generation (RAG), vector databases, Model Context Protocols (MCPs), orchestration libraries like LangChain and LangGraph, and AI agents. This lesson gives a concise, hands-on overview so you can understand how these pieces fit together and start building right away.
This lesson assumes no prior knowledge. It’s structured around a single, practical project that integrates fundamental AI concepts (tokens, embeddings, context windows, prompt design) with retrieval and orchestration (RAG, vector databases, LangChain/LangGraph, MCPs, and agents).
We’ll cover these topics and why they matter:
TopicWhat it isWhy it matters
Tokens, context windows, prompt designThe basic units and limits for language model input and strategies for guiding behaviorImpacts cost, capability, and response quality
EmbeddingsNumerical vectors that represent text semanticsEnables semantic search and similarity-based retrieval
Retrieval-Augmented Generation (RAG)Combining retrieval from a knowledge store with generation by a modelImproves factual accuracy and relevance for LLM outputs
Vector databasesStorage and indexing systems for embeddingsFast, scalable similarity search for RAG pipelines
LangChain / LangGraphOrchestration libraries for composing models, prompts, and toolsSimplifies building complex, multi-step AI workflows (agents)
MCPs (Model Context Protocols)Conventions for how models share context and toolsHelps agents coordinate model calls and external tools
AI AgentsSystems that use models + tools to perform tasks autonomouslyEnables multi-step, tool-enabled workflows like data lookups, API calls, and reasoning
We’ll progress in a practical order:
  1. Core AI fundamentals (tokens, embeddings, context windows, prompt design)
  2. Retrieval-Augmented Generation and vector databases — how embeddings are stored and searched
  3. Orchestration with LangChain and LangGraph, and how they help build agents
  4. MCPs and agent coordination across models and tools
  5. A single end-to-end project that ties these components together
Along the way you’ll see how each layer interacts with the others so you can design robust, production-ready AI applications that are both accurate and cost-effective. Useful references and deeper-dive resources are linked inline for each topic.
A chalkboard-style sketch showing a neural-network labeled "A.I." with arrows to terms like embeddings, tokens, RAG, prompt engineering and a globe below, plus a small doodle of a person on the left.

Watch Video