💪
3 Week Bootcamp: Building Realtime LLM Application
  • Introduction
    • Timelines and Structure
    • Course Syllabus
    • Meet your Instructors
    • Action Items
  • Basics of LLM
    • What is Generative AI?
    • What is a Large Language Model?
    • Advantages and Applications of Large Language Models
    • 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
      • Neural Networks and Transformers (Bonus Module)
      • Attention and Transformers (Bonus Module)
      • Multi-Head Attention, Transformers Architecture, and Further Reads (Bonus Module)
    • Graded Quiz 1
  • Prompt Engineering
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • Best Practices to Follow in Prompt Engineering
    • Token Limits in Prompts
    • Ungraded Prompt Engineering Excercise
      • Story for the Excercise: The eSports Enigma
      • Your Task
  • Retrieval Augmented Generation 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
    • 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)
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites
    • Dropbox Retrieval App in 15 Minutes
      • Building the app without Dockerization
      • Understanding Docker
      • Building the Dockerized App
    • Amazon Discounts App
      • How the Project Works
      • Repository Walkthrough
    • How to Run 'Examples'
  • Bonus Resource: Recorded Interactions from the Archives
  • Bootcamp Keynote Session on Vision Transformers
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Tracks for Submission
    • Final Submission
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  • 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

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.