What will you in LangChain 101 for Beginners (OpenAI / ChatGPT / LLMOps) Course
Build 3 real-world LLM applications using LangChain (Agents, Document Loader/Chatbot, and Code Interpreter).
Master prompt engineering techniques (Chain‑of‑Thought, ReAct, Few‑Shot) and understand the structure of the LangChain codebase.
Integrate memory, embedding-based RAG, vector stores (Pinecone, FAISS), and output parsing into your workflows.
Learn how to create, configure, and customize Chains, Agents, DocumentLoaders, PromptTemplates, and callback handlers.
Understand LLM theory and how context and prompts function under the hood, enabling smarter model design.
Discover advanced concepts including LangSmith, LangGraph introduction, and Model Context Protocol (MCP).
Program Overview
Module 1: Introduction & Setup
⏳ 30 minutes
Install Python, LangChain (v0.3+), and required APIs (OpenAI, Pinecone).
Get groundwork understanding of LangChain architecture and LLM theory.
Module 2: Build an Ice‑Breaker Agent
⏳ 120 minutes
Create an agent that scrapes LinkedIn/Twitter, finds social profiles, and generates personalized ice-breakers.
Incorporate Chains, Toolkits, and function-calling for external LLM tasks.
Module 3: Documentation Chatbot
⏳ 90 minutes
Load Python package docs, create embeddings, and build a chatbot with memory and RAG.
Use DocumentLoader, TextSplitter, VectorStore, memory, and streaming updates.
Module 4: Code Interpreter Chat Clone
⏳90 minutes
Build a lightweight version of ChatGPT’s code interpreter: streaming, file operations, code execution.
Integrate embedded agents and fine-tune prompt templates for code handling.
Module 5: Prompt Engineering & Theory
⏳60 minutes
Cover theories: chain-of-thought prompting, ReAct, few-shot, and parsing techniques.
Dive into LangChain’s MCP, LangSmith, and introduction to LangGraph.
Module 6: Debug, Extend & Best Practices
⏳60 minutes
Debug complex agents, add UI support via Streamlit, and refine models for robustness.
Walk through LangChain internals, unit tests, and tool chaining strategies.
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Job Outlook
High Demand: LangChain proficiency is in strong demand for LLM-driven app development roles.
Career Advancement: Empowers backend and ML engineers to build advanced AI systems.
Salary Potential: $100K–$180K+ roles in AI application and GenAI engineering.
Freelance Opportunities: Build chatbots, RAG systems, and custom LLM apps for clients.
Specification: LangChain 101 for Beginners (OpenAI / ChatGPT / LLMOps)
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