🚀
10 Days Realtime LLM Bootcamp
  • Introduction
    • Getting Started
    • Course Syllabus
    • Course Structure
    • Prerequisites
    • Greetings from your Instructors
    • First Exercise (Ungraded)
  • Basics of LLM
    • What is Generative AI?
    • What is a Large Language Model?
    • Advantages and Applications of Large Language Models
    • Bonus Resource: Multimodal LLMs
  • Word Vectors Simplified
    • What is a Word Vector
    • Word Vector Relationships
    • Role of Context in LLMs
    • Transforming Vectors into LLM Responses
      • Neural Networks and Transformers (Bonus Module)
      • Attention and Transformers (Bonus Module)
      • Multi-Head Attention and Further Reads (Bonus Module)
    • Let's Track Our Progress
  • Prompt Engineering
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • Best Practices to Follow in Prompt Engineering
    • Token Limits in Prompts
    • Prompt Engineering Excercise
      • Story for the Excercise: The eSports Enigma
      • Tasks in the Excercise
  • Retrieval Augmented Generation and LLM Architecture
    • What is Retrieval Augmented Generation (RAG)?
    • Primer to RAG Functioning and LLM Architecture: Pre-trained and Fine-tuned LLMs
    • In-Context Learning
    • High level LLM Architecture Components for In-context Learning
    • Diving Deeper: LLM Architecture Components
    • LLM Architecture Diagram and Various Steps
    • RAG versus Fine-Tuning and Prompt Engineering
    • Versatility and Efficiency in Retrieval-Augmented Generation (RAG)
    • Key Benefits of RAG for Enterprise-Grade LLM Applications
    • Similarity Search in Vectors (Bonus Module)
    • Using kNN and LSH to Enhance Similarity Search in Vector Embeddings (Bonus Module)
    • Track your Progress
  • Hands-on Development
    • Prerequisites
    • Dropbox Retrieval App in 15 Minutes
      • Building the app without Dockerization
      • Understanding Docker
      • Using Docker to Build the App
    • Amazon Discounts App
      • How the Project Works
      • Step-by-Step Process
    • How to Run the Examples
  • Live Interactions with Jan Chorowski and Adrian Kosowski | Bonus Resource
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Tracks for Submission
    • Final Submission
Powered by GitBook
On this page
  • Technical Explanation Made Simple
  • Now you know how Context Matters.
  1. Word Vectors Simplified

Role of Context in LLMs

PreviousWord Vector RelationshipsNextTransforming Vectors into LLM Responses

Last updated 1 year ago

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

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

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 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.