💪
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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  • Up Next: Explainer by Mike Chambers from AWS
  • Bonus Links
  1. Word Vectors Simplified

Transforming Vectors into LLM Responses

PreviousRole of Context in LLMsNextNeural Networks and Transformers (Bonus Module)

Last updated 1 year ago

Now that you're familiar with word vectors and the significance of context, it's vital to grasp other fundamental components like tokenizers and detokenizers before we investigate the sophisticated processes that power Large Language Models (LLMs).

Consider a tokenizer as a "sentence divider." Its job is to dissect sentences into smaller fragments such as words, characters, subwords, or symbols that the model can process. The specific approach varies based on the model's design and size. Conversely, detokenizers perform the opposite function; they assemble the pieces outputted by the LLM into coherent sentences that we can comprehend. This step is essential for LLMs to convert human language into executable actions.

Up Next: Explainer by Mike Chambers from AWS

Upcoming: Informative Video by Mike Chambers from AWS To further enhance your understanding, we will turn to an instructive video by Mike Chambers. In this presentation, he clarifies the chain of events triggered by your 'prompt' (the text input you provide) to an LLM.

While the underlying mathematics might be complex, the main objective remains simple: predicting words. The video will walk you through the process of how your prompts are handled to produce intelligible text responses. This serves as a precursor to our forthcoming discussions about Prompt Engineering and LLM workflows. By doing so, we aim to present a unified view of the operational aspects of these models.

(Credits: Build on AWS)

Here you see how a Large Language Model’s job is to predict the next word based on the context.

Now that you understand the role of "context," you might want to grasp some concepts to appreciate how these models work at a granular level. These are bonus resources that are not necessary for you to complete, given the timelines of this course.

  • Attention in Large Language Models: Imagine being in a room where multiple conversations are happening. Your ability to focus on one conversation over the others is similar to how Attention works in neural networks. It allows the model to 'focus' on relevant parts of the input for tasks.

  • Encoder-Decoder Architecture: In this, an encoder translates the input (e.g., a sentence) into a fixed-size context vector. The decoder takes this context vector to generate an output sequence (e.g., a translated sentence). When the attention mechanism is in action, it guides the Decoder to focus on certain parts of the Encoder’s output, enhancing the translation or text generation task. The concept of Attention complements the Encoder-Decoder architecture, making it more effective and efficient. This architecture is a building block for LLMs such as GPT-3.5.

Bonus Links

If you're interested in delving further into the details, you may find the following links on embeddings, attention mechanisms, and encoder-decoder architecture beneficial. A foundational understanding of neural networks, backpropagation, the softmax function, and cross-entropy will enhance your comprehension of these resources. These topics are not the primary focus of this course, so they're provided as bonus links.

  • Understanding Transformers: Check the Bonus Module Right Ahead.

| Short Video by Google Cloud

| Video by StatQuest

| Video by StatQuest

| Read the Paper on ArXiv

| Watch the seminar by Stanford Online

Attention mechanism: Overview
Word2Vec and Word Embeddings
Seq2Seq Encoder-Decoder Neural Networks
Attention is all you need
Attention is all you need