What will you in LangChain Mastery: Build GenAI Apps with LangChain &Pinecone Course
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Grasp LangChain fundamentals for building powerful LLM applications with Python.
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Integrate Pinecone and Chroma vector databases for semantic search and RAG workflows.
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Develop real-world apps like document summarizers, chatbots, and RAG pipelines step-by-step using Streamlit.
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Apply prompt engineering techniques within LangChain—stuff, map_reduce, refine, and agent strategies.
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Learn to deploy interactive web UIs using Streamlit and AI coding assistants like Jupyter AI.
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Work with OpenAI’s GPT models and Google Gemini within LangChain projects.
Program Overview
Module 1: LangChain & Environment Setup
⏳ 30 minutes
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Install Python, LangChain, Pinecone SDK, and configure API keys for OpenAI/Gemini.
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Understand the architecture of chain, agent, and vector workflows used in later modules.
Module 2: Building a Document Summarizer
⏳ 60 minutes
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Create a summarization system with LangChain chains (stuff, map/ reduce, refine).
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Integrate vector embeddings and perform QA on large text documents.
Module 3: RAG & Vector Stores
⏳ 60 minutes
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Setup and query Pinecone and Chroma for vector indexing.
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Build Retrieval-Augmented Generation components connecting text to LLM outputs.
Module 4: LangChain Agents & Chains
⏳ 75 minutes
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Form multi-step agent workflows using tools, prompt templates, and function calling.
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Use Jupyter AI assistants for interactive agent testing and refinement.
Module 5: Interactive Streamlit Front-End
⏳ 60 minutes
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Build web interfaces for LLM apps: Streamlit widgets, session states, callbacks.
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Deploy chatbot, file uploader, and summarizer apps via Streamlit.
Module 6: Prompt Engineering & Best Practices
⏳ 45 minutes
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Explore prompt templates, few-shot prompting, refinement, and chain-of-thought.
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Learn to troubleshoot prompt performance and context in real applications.
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Job Outlook
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High demand for LLM and RAG engineers capable of building AI-powered systems end-to-end.
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Valuable skill for AI product development, ML engineering, and conversational AI roles.
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Salary potential: $100K–$180K+ in AI-focused software engineering careers.
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Freelance opportunities: RAG systems, document AI, chatbot development, and custom AI apps.
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Related Reading
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What Is Product Management? – Learn how product management principles help guide the design, deployment, and scaling of AI-powered applications successfully.
