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3 Week Bootcamp: Building Realtime LLM Application
  • Introduction
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    • 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
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    • Role of Context in LLMs
    • Transforming Vectors into LLM Responses
      • Neural Networks and Transformers (Bonus Module)
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    • 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
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    • High-level LLM Architecture Components for In-context Learning
    • Diving Deeper: LLM Architecture Components
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    • 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
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    • Dropbox Retrieval App in 15 Minutes
      • Building the app without Dockerization
      • Understanding Docker
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    • Amazon Discounts App
      • How the Project Works
      • Repository Walkthrough
    • How to Run 'Examples'
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  • Bootcamp Keynote Session on Vision Transformers
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  1. Prompt Engineering

Token Limits in Prompts

PreviousBest Practices to Follow in Prompt EngineeringNextUngraded Prompt Engineering Excercise

Last updated 1 year ago

By now, you know LLMs are the AI powerhouses trained on heaps of data, and prompts are what enable you to make the most out of them.

However, it’s important to learn that different LLMs have specific token limits that define their performance. Ideally, when you’re creating your prompt, you need to ensure that you’re not crossing these token limits. Let’s understand this concept quickly.

  • Token Limits: These dictate how many tokens an LLM can handle in one go.

  • Estimated Word Counts: This refers to the approximate number of words that can fit within a model’s token limit. It helps you gauge how much content you can generate or process.

If you try copy-pasting a long Wikipedia article (for example, that of Google), you’ll notice an error.

Think of token and word counts as your LLM's capacity. While tokens define the technical limit, estimated word counts translate this into a more human-understandable measure.

Why It Matters: Knowing the estimated word count helps you manage your input prompts and outputs more efficiently.

Comparative Analysis: Token and Estimated Word Counts in a Few Leading LLMs

While the foundational knowledge provided is adequate for course progression, further exploration of tokens is available in the documentation linked below.

Tokens and Efficient Prompt Design | Open AI
LLM AI Tokens | Microsoft