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Spring of Realtime LLMs
  • Get started with the Bootcamp!
    • Course Structure
    • Course Syllabus and Timelines
    • Know your Educators
    • Action Items and Prerequisites
    • Bootcamp Introductory Session | National Science Day
  • 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 (Early Access)
    • 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
  • 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 the 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)
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites
    • 1 – Dropbox Retrieval App
      • Understanding Docker
      • Build the Dockerized App
      • Retrofitting our Dropbox app
    • 2 – Amazon Discounts App
      • How the Project Works
      • Building the App
    • 3 – RAG with Open Source and Running "Examples"
    • 4 (Bonus Section) – Realtime RAG with LlamaIndex and Pathway
      • Understanding the Basics
      • Implementation
      • Sample Business Use-case
  • Bonus Resource: Recorded Interactions from the Archives
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Suggested Tracks for Ideation
    • Form for Submission
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  1. Prompt Engineering and Token Limits (Early Access)
  2. Prompt Engineering Excercise (Ungraded)

Your Task for the Module

PreviousStory for the Excercise: The eSports EnigmaNextRAG and LLM Architecture

Last updated 1 year ago

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Subtask 1: Plot Extension

Design a prompt to generate a continuation of the mystery story that uncovers another layer of the hacking plot. Perhaps Valérie finds out there's a mole in her organization, or maybe Sophie encounters another similar case. Craft a prompt that nudges the story into revealing this new dimension. Evaluate how seamlessly the new narrative fits with the original story.

Subtask 2: Few-Shot Prompting for Character Analysis

Utilize a few-shot prompt that instructs ChatGPT to analyze the main characters' emotional states at crucial points in the story. For instance, ask the model to perform sentiment analysis on Valérie when she discovers the hacking, and Sophie when she finally solves the case. Note the efficacy of few-shot prompting in eliciting nuanced character analysis.

Subtask 3: Limitation Recognition Due to Outdated Data

Pose a question about the use of AI and machine learning algorithms in contemporary eSports as depicted in the story, asking for the latest advancements as of 2023. Evaluate ChatGPT's response for potential inaccuracies or outdated information given its last training data is from April 2023 (this date is certainly expected to change from time to time). Discuss how this limitation affects the believability of the plot and the technological aspects described in the story.

For each subtask, provide the prompt you used, summarize the response, and give a thorough analysis of how well ChatGPT performed in terms of context, plot coherency, and technological accuracy.

where you can submit your responses.

Alternate Task

Explore a new blog post on a topic you're unfamiliar with. For example below is a blog on Bollinger Bands and streaming within Jupyter Notebooks.

If the content is already known to you, consider diving into subjects like Docker or Streamlit, tools we'll soon utilize. The goal is to deepen your understanding of novel topics or discover how to engage with their communities through LLM assistance. However, be cautious of potential inaccuracies or "hallucinations" from LLM responses. Validate your findings with further internet research to ensure accuracy. Continue querying and learning until you've grasped the concept. Exciting, isn't it?

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Python in Finance: Real Time Data Streaming within Jupyter Notebook - KDnuggetsKDnuggets
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