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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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Prompt Engineering and Token Limits

PreviousGroup Session RecordingNextWhat is Prompt Engineering

Last updated 1 year ago

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Welcome to the module where we learn the best practices for interacting with LLMs – often clubbed under the umbrella of "prompt engineering"!

Here, you will venture into the realm of prompt engineering, the technique behind optimizing interactions with Large Language Models (LLMs).

In practice, it's like learning communication with humans – the more you do, the better you become at it. By the course's end, if you can understand prompt engineering and use it proactively, you'll find that most basic doubts that come across your journey will be addressed as you can figure out things yourself.

For starters, this module unravels the science and art of crafting precise context to garner desired outcomes from LLMs like Google Gemini, Claude, or ChatGPT. By diving deep into the principles of in-context learning and the nuances of prompt design, you'll gain insights into the intricate dance between human queries and machine-generated responses.

As you journey through this section, you'll discover the foundational concepts that underpin effective communication with LLMs.