Topics

  • What is an LLM based system?
  • How is an LLM based system different from a traditional software system?
  • What is context, and how this context is provided to the LLM - prompts, RAG, MCP
  • The 8 steps involved in end to end working of an LLM to output result of a query. - training using corpus, Tokenization, Embedding, Vector DB creation & RAG, Query & Prompts, Search, Retrieval, Output.
  • What is an agentic AI system?
  • What is Lanchain? The elements of langchain - Prompts, LLM, chain, Vector Db, Index, Memory state, data loader, data splitter: https://www.youtube.com/watch?v=1bUy-1hGZpI&t=241s
  • What is Langraph? https://www.youtube.com/watch?v=qAF1NjEVHhY
  • Code walkthrough
  • Streaming in langchain - https://www.youtube.com/watch?v=gr5CGL4_jpY
  • Build with langhcain playlist - https://www.youtube.com/watch?v=mmBo8nlu2j0&list=PLfaIDFEXuae06tclDATrMYY0idsTdLg9v
    • Auto Prompt builder
    • RAG app with type script
    • SQL research agent
    • skeleton of thoughts
    • building a research assistant
    • build and deploy RAG app with pinecone serverless
    • build a web RAG chatbot using langchain
    • open source RAG with Nomic’s new embedding model

Working code

  1. RAG -> Langgraph workflow -> Decide to choose RAG or simple LLM