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Spring of Realtime LLMs
  • Get started with the Bootcamp!
    • Course Structure
    • Course Syllabus and Timelines
    • Know your Educators
    • Action Items and Prerequisites
    • Bootcamp Introductory Session | National Science Day
  • 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 (Early Access)
    • 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
  • 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 Architecture with the Key Components
    • RAG versus Fine-Tuning and Prompt Engineering
    • Versatility and Efficiency in RAG
    • Key Benefits of using RAG in an Enterprise/Production Setup
    • 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
    • 1 – Dropbox Retrieval App
      • Understanding Docker
      • Build the Dockerized App
      • Retrofitting our Dropbox app
    • 2 – Amazon Discounts App
      • How the Project Works
      • Building the App
    • 3 – RAG with Open Source and Running "Examples"
    • 4 (Bonus Section) – Realtime RAG with LlamaIndex and Pathway
      • Understanding the Basics
      • Implementation
      • Sample Business Use-case
  • Bonus Resource: Recorded Interactions from the Archives
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Suggested Tracks for Ideation
    • Form for Submission
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  • Technical Explanation Made Simple
  • Now you know how Context Matters

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  1. Word Vectors, Simplified

Role of Context in LLMs

PreviousWord Vector RelationshipsNextTransforming Vectors into LLM Responses

Last updated 1 year ago

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Let's dive a bit deeper into the world of word vectors and explore how context comes into play.

This is the same example we depicted during our introductory live session on National Science Day.

Imagine you're trying to understand the word "apple." Without context, it could be a fruit or a tech company. But what if I say, "I ate an apple"? Now it's clear, right? Context helps us make sense of words, and it's no different for large language models.

Technical Explanation Made Simple

In general, large language models like GPT-4 or Llama use various techniques to understand the context surrounding each word. For instance, GPT-4 leverages a popular and efficient technique called the "attention mechanism," which helps the model focus on different parts of the text to understand it better. However, older models might use other strategies like Recurrent Neural Networks (RNNs) or Long Short-Term Memory Networks (LSTMs) to capture context differently.

Whether it's attention mechanisms or RNNs, the goal is the same: to give the model a better understanding of how words relate to each other. This understanding is crucial for tasks like language translation, text summarisation, and question answering.

Now you know how Context Matters

Context is not just a technical requirement but a functional necessity. By understanding the context, these models can perform tasks ranging from simple ones like spelling correction to complex ones like reading comprehension.

So, the next time you see a language model perform a task incredibly well, remember that it's not just about the individual words but also the context in which they are used.