AI Agents & RAG

AI AgentsRAG PipelinesVector SearchLLMOps

Build intelligent AI agents and RAG systems from fundamentals to production. Master tool use, multi-agent architectures, vector databases, and deployment strategies.

Free Tutorial

Learn AI Agents & RAG - Build Intelligent LLM Applications

Master AI Agents and Retrieval-Augmented Generation for modern AI applications. Learn to build autonomous agents with tool use, implement RAG pipelines with vector databases, orchestrate multi-agent systems, and deploy production-grade AI applications with guardrails and monitoring.

Prerequisites

Before learning AI Agents & RAG, you should have a solid understanding of TypeScript or Python, REST APIs, and basic familiarity with LLMs and prompting.

AI Fundamentals TypeScript Tutorial

What You'll Learn

✓ AI agent architectures & patterns
✓ ReAct, planning, and reasoning
✓ Tool use & function calling
✓ Multi-agent collaboration systems
✓ RAG pipelines end-to-end
✓ Vector databases & embeddings
✓ Advanced retrieval & reranking
✓ LLM orchestration frameworks
✓ Guardrails & safety for AI apps
✓ Production deployment & LLMOps

Course Topics

Understand what AI agents are, their types, capabilities, and the current landscape of autonomous AI systems

Explore ReAct, Plan-and-Execute, multi-agent, and tool-use architectural patterns for building AI agents

Master the Reasoning + Acting loop pattern for building agents that think before they act

Planning and Reasoning

Learn chain-of-thought, tree-of-thought, and self-reflection techniques for advanced agent reasoning

Tool Use and Function Calling

Master OpenAI and Anthropic function calling APIs, tool schemas, and building robust tool-using agents

Build collaborative AI systems with CrewAI, AutoGen, and LangGraph multi-agent patterns

Implement short-term, long-term, and episodic memory to build agents that remember and learn

Understand Retrieval-Augmented Generation architecture, why it matters, and how it works end-to-end

Master fixed-size, semantic, recursive, and parent-child chunking strategies for optimal RAG retrieval

Compare OpenAI, Cohere, and open-source embedding models for semantic search and RAG applications

Vector Databases Overview

Compare Pinecone, Weaviate, ChromaDB, Qdrant, and pgvector for building RAG applications

Step-by-step guide to setting up and using Pinecone for production RAG applications

Build local-first RAG applications with ChromaDB, the open-source embedding database

Combine keyword-based BM25 search with semantic vector search for superior retrieval quality

Improve retrieval precision with cross-encoder reranking, Cohere Rerank, and ColBERT

Measure RAG quality with RAGAS metrics including faithfulness, relevancy, and answer precision

Building Chatbots with RAG

Build end-to-end conversational chatbots with context retrieval, memory, and streaming responses

Compare LangChain, LlamaIndex, and Semantic Kernel for building production LLM applications

Guardrails and Safety

Implement input/output validation, content filtering, and safety guardrails for AI applications

Learn the LLM lifecycle including monitoring, evaluation, deployment, and cost management

Learn when and how to fine-tune LLMs, including LoRA, QLoRA, and best practices for custom models

Benchmark and evaluate AI agents with metrics, human evaluation, and automated testing frameworks

Production AI Applications

Go from prototype to production with scaling, cost management, reliability, and deployment strategies

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Frequently Asked Questions

What are AI agents?

AI agents are autonomous software systems powered by large language models (LLMs) that can perceive their environment, make decisions, and take actions to achieve specific goals. Unlike simple chatbots, AI agents can use tools, access external data, maintain memory across interactions, and chain multiple reasoning steps together to solve complex problems.

What is RAG (Retrieval-Augmented Generation)?

RAG (Retrieval-Augmented Generation) is a technique that enhances LLM responses by retrieving relevant information from external knowledge bases before generating an answer. It combines a retrieval system (typically using vector databases and embeddings) with a generative model, allowing AI applications to provide accurate, up-to-date answers grounded in specific documents or data sources.

How do vector databases work?

Vector databases store data as high-dimensional numerical vectors (embeddings) and enable similarity search using distance metrics like cosine similarity. When you query a vector database, it finds the most semantically similar vectors to your query, making it ideal for RAG pipelines, recommendation systems, and semantic search. Popular vector databases include Pinecone, ChromaDB, Weaviate, and Qdrant.

What is the difference between fine-tuning and RAG?

Fine-tuning modifies the LLM's weights by training it on domain-specific data, permanently embedding knowledge into the model. RAG keeps the model unchanged and instead retrieves relevant context at query time from external sources. RAG is preferred when knowledge changes frequently, you need source attribution, or you want to avoid the cost of fine-tuning. Fine-tuning is better for teaching the model new behaviors, styles, or specialized reasoning patterns.

Ready to Build AI Agents?

Begin your journey with understanding what AI agents are, how they work, and the current landscape of autonomous AI systems.

Start Learning AI Agents →