📘
Winter LLM Bootcamp
  • Welcome to the course. Bienvenue!
    • Course Structure
    • Course Syllabus and Timelines
    • Know your Educators
    • Action Items and Prerequisites
    • Bootcamp Kick-Off Session
  • Basics of LLMs
    • What is Generative AI?
    • What is a Large Language Model?
    • Advantages and Applications of LLMs
    • Bonus Resource: Multimodal LLMs and Google Gemini
  • Word Vectors, Simplified!
    • What is a Word Vector
    • Word Vector Relationships
    • Role of Context in LLMs
    • Transforming Vectors into LLM Responses
    • Bonus Section: Overview of the Transformers Architecture
      • Attention Mechanism
      • Multi-Head Attention and Transformers Architecture
      • Vision Transformers
    • Graded Quiz 1
  • Prompt Engineering and Token Limits
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • Best Practices to Follow
    • Token Limits and Hallucinations
    • Prompt Engineering Excercise (Ungraded)
      • Story for the Excercise: The eSports Enigma
      • Your Task for the Module
  • Retrieval Augmented Generation (RAG) and LLM Architecture
    • What is Retrieval Augmented Generation (RAG)
    • Primer to RAG: Pre-trained and Fine-Tuned LLMs
    • In-Context Learning
    • High-level LLM Architecture Components for In-Context Learning
    • Diving Deeper: LLM Architecture Components
    • Basic RAG/LLM Architecture Diagram with Key Steps
    • RAG versus Fine-Tuning and Prompt Engineering
    • Versatility and Efficiency in RAG
    • Understanding Key Benefits of Using RAG in Enterprises
    • Hands-on Demo: Performing Similarity Search in Vectors (Bonus Module)
    • Using kNN and LSH to Enhance Similarity Search (Bonus Module)
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites
    • Dropbox Retrieval App
      • Understanding Docker
      • Building the Dockerized App
      • Retrofitting our Dropbox app
    • Amazon Discounts App
      • How the project works
      • Repository Walkthrough
    • How to Run 'Examples'
    • Bonus Section: Real-time RAG with LlamaIndex and Pathway
  • Bonus Resource: Recorded Interactions from the Archives
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Suggested Tracks for Ideation
    • Form for Submission
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  1. Retrieval Augmented Generation (RAG) and LLM Architecture

In-Context Learning

PreviousPrimer to RAG: Pre-trained and Fine-Tuned LLMsNextHigh-level LLM Architecture Components for In-Context Learning

Last updated 1 year ago

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In this video, you'll be introduced to the concept of in-context learning through prompts. Anup explains how this form of learning is scalable, particularly when dealing with vast amounts of data.

This becomes especially relevant when we recall our earlier discussions on Retrieval Augmented Generation (RAG). Understanding in-context learning amplifies the efficacy of technologies like RAG in Large Language Models.