Tool Calling & RAG Systems

About Course

Tool Calling & RAG Systems

Build Actionable, Knowledge-Grounded AI Agents

Short Description (Course Listing):

Learn how to transform Large Language Models into real-world AI agents that can call tools, access live data, and retrieve domain-specific knowledge using Tool Calling and Retrieval-Augmented Generation (RAG). This hands-on course teaches you to build production-ready AI systems that go far beyond chatbots.

Full Course Description :

Modern AI applications require more than text generation — they require action, accuracy, and real-time intelligence.

In this practical, implementation-focused course, you will learn how to:

• Enable LLMs to call external functions and APIs

• Integrate real-time data sources into AI workflows

• Build knowledge-grounded systems using RAG

• Design scalable vector search pipelines

• Reduce hallucinations and improve factual accuracy

You will move from theory to practice by building multi-tool AI agents and a document question-answering system powered by RAG.

Part 1: Tool Calling & Function Integration

This section focuses on turning LLMs into action-capable agents.

Understanding Tool Calling Mechanisms in LLMs

Explore how LLMs decide when and how to invoke external tools using structured outputs and reasoning-based tool selection.

Defining Function Schemas and Parameters

Learn to design reliable function interfaces using JSON schemas, parameter validation, and type-safe inputs to ensure production-ready automation.

API Integration with AI Agents

Connect your AI to real-world systems through REST APIs, secure key handling, response parsing, and error management.

Real-Time Data Retrieval with Tools

Enable your agent to access live information such as weather data, calculations, and external services for dynamic decision-making.

🛠 Workshop: Multi-Tool AI Agent

Build a working AI agent that:

• Calls a weather API for live forecasts

• Uses a calculator tool for mathematical reasoning

• Automatically selects the correct tool

• Returns structured and human-readable outputs

Part 2: Retrieval-Augmented Generation (RAG)

This section teaches you how to make AI knowledge-aware and factually grounded.

What is RAG and Why It Matters

Understand how RAG combines retrieval and generation to reduce hallucinations and provide context-aware responses.

Vector Databases & Embeddings (Pinecone, Chroma)

Learn semantic search using embeddings and implement vector storage, similarity search, and metadata filtering with Pinecone and Chroma.

Building Knowledge Bases for AI Agents

Create domain-specific AI systems by ingesting documents, preprocessing text, and designing scalable indexing pipelines.

Chunking Strategies & Retrieval Optimization

Improve retrieval accuracy using fixed and semantic chunking, overlap techniques, top-k tuning, and reranking methods.

🛠 Practical: Document Q&A System with RAG

Implement a complete RAG pipeline that:

• Ingests documents

• Generates embeddings

• Stores vectors in a database

• Retrieves relevant context

• Produces grounded answers with citations

Learning Outcomes

By the end of this course, you will be able to:

✔ Build tool-enabled AI agents that call APIs and functions
✔ Integrate real-time data into LLM workflows
✔ Design and deploy vector-based knowledge systems
✔ Implement production-grade RAG pipelines
✔ Develop accurate, scalable, and trustworthy AI applications

Who This Course Is For

• AI/ML engineers

• Backend and full-stack developers

• Prompt engineers

• Data engineers

• Anyone building real-world AI agents