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3-Week Building LLMs Bootcamp
  • Welcome to the Bootcamp
    • Course Structure
    • Course Syllabus and Timelines
    • Know your Educators
    • Action Items and Prerequisites
    • Kick Off Session at Tryst 2024
  • 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
    • Group Session Recording
  • 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
    • Group Session Recording
  • Prompt Engineering and Token Limits
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • For Starters: Best Practices to Follow
    • Navigating Token Limits
    • Hallucinations in LLMs
    • Prompt Engineering Excercise (Ungraded)
      • Story for the Excercise: The eSports Enigma
      • Your Task for the Module
    • Group Session Recording
  • 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 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)
    • Bonus Video: Implementing End-to-End RAG | 1-Hour Session
    • Group Session Recording
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites
    • 1 – Dropbox Retrieval App
      • Understanding Docker
      • Building the Dockerized App
      • Retrofitting your Dropbox app
    • 2 – Amazon Discounts App
      • How the Project Works
      • Building the App
    • 3 – RAG with Open Source and Running "Examples"
    • 4 (Bonus) – Realtime RAG with LlamaIndex/Langchain and Pathway
      • Understanding the Basics
      • Implementation with LlamaIndex and Langchain
    • Building LLM Apps with Open AI Alternatives using LiteLLM
  • Bonus Resource: Recorded Interactions from the Archives
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Suggested Tracks for Ideation
    • Sample Projects and Additional Resources
    • 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 at Tryst 2024.

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.