📘
Winter LLM Bootcamp
  • Welcome to the course. Bienvenue!
    • Course Structure
    • Course Syllabus and Timelines
    • Know your Educators
    • Action Items and Prerequisites
    • Bootcamp Kick-Off Session
  • 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
  • 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
  • Prompt Engineering and Token Limits
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • Best Practices to Follow
    • Token Limits and Hallucinations
    • Prompt Engineering Excercise (Ungraded)
      • Story for the Excercise: The eSports Enigma
      • Your Task for the Module
  • Retrieval Augmented Generation (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/LLM Architecture Diagram with Key Steps
    • RAG versus Fine-Tuning and Prompt Engineering
    • Versatility and Efficiency in RAG
    • Understanding Key Benefits of Using RAG in Enterprises
    • Hands-on Demo: Performing Similarity Search in Vectors (Bonus Module)
    • Using kNN and LSH to Enhance Similarity Search (Bonus Module)
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites
    • Dropbox Retrieval App
      • Understanding Docker
      • Building the Dockerized App
      • Retrofitting our Dropbox app
    • Amazon Discounts App
      • How the project works
      • Repository Walkthrough
    • How to Run 'Examples'
    • Bonus Section: Real-time RAG with LlamaIndex and Pathway
  • Bonus Resource: Recorded Interactions from the Archives
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Suggested Tracks for Ideation
    • Form for Submission
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On this page
  • Comparative Analysis: Token and Estimated Word Counts in a Few Leading LLMs
  • Concept of Hallucinations
  • Additional Links on Token Limits

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  1. Prompt Engineering and Token Limits

Token Limits and Hallucinations

PreviousBest Practices to FollowNextPrompt Engineering Excercise (Ungraded)

Last updated 1 year ago

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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

LLMs process prompts based on vast data sets, leading to token limits that cap the amount of text (input and output) they can handle in one interaction. Understanding these limits is essential for crafting effective prompts without exceeding the model's processing capacity.

Concept of Hallucinations

This happens because LLMs draw on patterns in their training data, not factual accuracy or logical reasoning. Therefore, while LLMs can produce remarkably coherent text, they can also "hallucinate" details, especially when dealing with topics outside their training data or when prompts lack specificity. Managing these hallucinations and token-limit constraints are crucial for using LLMs effectively, and that's where techniques like Retrieval-Augmented Generation (RAG) help. We'll see it very soon and understand how it aims to mitigate such issues by combining LLM outputs with real-time data retrieval.

Additional Links on Token Limits

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

Hallucinations in LLMs occur when the model generates false or misleading information, and sometimes in a convincing way.

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