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