What you'll learn:
- Running LLMs in Local Machine for development of LLM application
- Understand the power of Langchain for building Local LLM application
- Understand Chain, Prompts, ChatPromptTemplates, ChatMessageHistory
- Building Chatbots with Historical Information with Langchain
- Building RAG application with Vector stores, Embedding and Local LLMs
- Understanding and Building Tools for LLMs
- Building AI Agents with Tooling support for LLMs
- Testing/Evaluating AI Agent & RAG Application with RAGAs
Build & Test AI Agents, Chatbots, and RAG with Ollama & Local LLMs
This course is designed for complete beginners—even if you have zero knowledge of LangChain, you’ll learn step by step how to build LLM-based applications using local Large Language Models (LLMs).
The course is fully updated with LangChain v1.0.3
We’ll go beyond development and dive into evaluating and testing AI agents, RAG applications, and chatbots using RAGAs to ensure they deliver accurate and reliable results, following key industry metrics for AI performance.
What You’ll Learn:
Fundamentals of LangChain & LangSmith
Chat Message History in LangChain for storing conversation data
Running Parallel & Multiple Chains (RunnableParallels, etc.)
Building Chatbots with LangChain & Streamlit (with message history)
Understanding Tools and Tool chains in LLM
Building Tools and Custom Tools for LLM
Creating AI Agents using LangChain
Implementing RAG with vector stores & local LLM embeddings
Using AIAgents and RAGwith Tooling while building LLMApps
Optimizing & Debugging AI applications with LangSmith
Evaluating & Testing LLM applications with RAGAs
Real-world projects & hands-on testing strategies
Assessing RAG & AI Agents with RAGAs
This entire course is taught inside Jupyter Notebook with Visual Studio, providing an interactive, guided experience where you can run the code seamlessly and follow along effortlessly.
By the end of this course, you’ll be able to build, test, and optimize AI-powered applications with confidence!
