BR Academy

Building AI Agents: Tool Use, RAG and Multi-Agent Systems

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← Building AI Agents: Tool Use, RAG and Multi-Agent Systems

1Foundations u2013 LLM Capabilities and Limits0/3

LLM APIs and token economics15m
Hallucination, reasoning gaps, and when agents help
The planning loop, tool use, and memory
Module quiz

2Tool Use and Function Calling0/4

OpenAI, Anthropic, and open-source function schemas
Forcing JSON responses and validating agent decisions
Wrapping APIs, databases, and code execution safely
Sequential, parallel, and conditional tool chains
Module quiz

3Retrieval-Augmented Generation (RAG)0/5

Semantic, sliding-window, and recursive chunking
Choosing models, dimensionality, and similarity search
Pinecone, Weaviate, Milvus, and local alternatives
BM25, dense retrieval, and cross-encoder reranking
Measuring retrieval quality and end-to-end accuracy
Module quiz

4Memory, Planning, and Multi-Agent Orchestration0/4

Short-term, long-term, and episodic memory patterns
ReAct, chain-of-thought, and tree-of-thought
Hierarchical, peer-to-peer, and market-based orchestration
Routing decisions, context passing, and state management
Module quiz

5Evaluation, Cost Control, and Observability0/3

Unit tests for agents, golden datasets, and metrics
Token budgets, caching, batching, and model selection
Tracing agent calls, debugging failures, and monitoring drift
Module quiz

6Safety, Security, and Deployment0/4

Attack vectors and input validation strategies
Filtering unsafe responses and enforcing constraints
FastAPI, async patterns, rate limiting, and scaling
Monitoring, alerting, cost tracking, and user feedback loops
Module quiz

7Capstone u2013 Build and Ship a Multi-Agent Service0/1

Design and deploy a live multi-agent system
Module quiz

Retrieval-Augmented Generation (RAG)·Lesson 9 of 24·18 min

Choosing models, dimensionality, and similarity search

This lesson is for enrolled learners

Concept An embedding model converts a chunk of text into a fixed-length vector of numbers such that semantically similar text produces nearby vectors in that vector space. This is the mechanism that lets retrieval match a user's question to a relevant document chunk even when they don't share any exact keywords — "how do I get my money back" can……

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