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LangChain Mastery: Build GenAI Apps with LangChain &Pinecone

A robust and up-to-date LangChain bootcamp that equips developers to create real-world LLM applications using vector databases and interactive UIs.

access

Lifetime

level

Beginner

certificate

Certificate of completion

language

English

What will you in LangChain Mastery: Build GenAI Apps with LangChain &Pinecone Course

  • Grasp LangChain fundamentals for building powerful LLM applications with Python.

  • Integrate Pinecone and Chroma vector databases for semantic search and RAG workflows.

  • 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.

  • Learn to deploy interactive web UIs using Streamlit and AI coding assistants like Jupyter AI.

  • Work with OpenAI’s GPT models and Google Gemini within LangChain projects.

Program Overview

Module 1: LangChain & Environment Setup

⏳ 30 minutes

  • Install Python, LangChain, Pinecone SDK, and configure API keys for OpenAI/Gemini.

  • Understand the architecture of chain, agent, and vector workflows used in later modules.

Module 2: Building a Document Summarizer

⏳ 60 minutes

  • Create a summarization system with LangChain chains (stuff, map/ reduce, refine).

  • Integrate vector embeddings and perform QA on large text documents.

Module 3: RAG & Vector Stores

⏳ 60 minutes

  • Setup and query Pinecone and Chroma for vector indexing.

  • Build Retrieval-Augmented Generation components connecting text to LLM outputs.

Module 4: LangChain Agents & Chains

⏳ 75 minutes

  • Form multi-step agent workflows using tools, prompt templates, and function calling.

  • Use Jupyter AI assistants for interactive agent testing and refinement.

Module 5: Interactive Streamlit Front-End

⏳ 60 minutes

  • Build web interfaces for LLM apps: Streamlit widgets, session states, callbacks.

  • Deploy chatbot, file uploader, and summarizer apps via Streamlit.

Module 6: Prompt Engineering & Best Practices

⏳ 45 minutes

  • Explore prompt templates, few-shot prompting, refinement, and chain-of-thought.

  • Learn to troubleshoot prompt performance and context in real applications.

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Job Outlook

  • High demand for LLM and RAG engineers capable of building AI-powered systems end-to-end.

  • Valuable skill for AI product development, ML engineering, and conversational AI roles.

  • Salary potential: $100K–$180K+ in AI-focused software engineering careers.

  • Freelance opportunities: RAG systems, document AI, chatbot development, and custom AI apps.

9.7Expert Score
Highly Recommended
A practical, project-focused course that empowers developers to build production-level LangChain apps with Pinecone.
Value
9.3
Price
9.5
Skills
9.7
Information
9.6
PROS
  • Hands-on, real-world projects including summarizers and RAG chat.
  • Updated with the latest LangChain, OpenAI, and Gemini integrations.
  • Strong emphasis on UI development and prompt engineering workflows.
CONS
  • Presumes solid Python background; not suited for complete beginners.
  • Lacks deep machine learning theory—focuses strictly on LangChain and implementation.

Specification: LangChain Mastery: Build GenAI Apps with LangChain &Pinecone

access

Lifetime

level

Beginner

certificate

Certificate of completion

language

English

LangChain Mastery: Build GenAI Apps with LangChain &Pinecone
LangChain Mastery: Build GenAI Apps with LangChain &Pinecone
Course | Career Focused Learning Platform
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