Become a Prompt Engineer: Prompt Engineering & LLMs Track

In this Prompt Engineering track, you'll learn essential skills, tools, and techniques for working with LLM-enabled applications.

Midjourney V5

Prompt engineering and large language models (LLMs) have emerged as one of the most valuable and versatile skills to have in your toolkit.

With the pace of AI advancing at incredible rates and showing no signs of slowing down, it's become quite clear that those who know how to effectively work with large language models have a serious edge in their careers.

Master the art and science of prompt engineering and harness the power of large language models (LLMs) to build powerful AI applications.

In this Prompt Engineering track, you'll learn essential skills, tools, and techniques for working with and building LLM-enabled applications.

We'll start with the basics of generative AI & LLMs, discuss key concepts in prompt engineering, and build real-world applications using GPT-3, GPT-4, Whisper, LangChain, Pinecone, ChatGPT Plugins, and more.

Project-based learning

A few of the projects we build in the track include:

  • Summarizing YouTube videos with OpenAI's Whisper & GPT-3
  • Building a PDF to Q&A Assistant Using Embeddings & GPT-3
  • Building a GPT-3 Enabled Research Assistant with LangChain & Pinecone
  • GPT-4 & Pinecone: Turning Any Website into an AI Assistant
  • Building an AI News Assistant ChatGPT Plugin
  • Building a Stock Screener ChatGPT Plugin
  • AutoGPT & LangChain: Building an Automated Research Assistant

Throughout this Prompt Engineering Track, you'll explore various aspects of working with large language models, including:

  • Working with popular foundational models such as GPT-3, GPT-4, and ChatGPT
  • Using LangChain, a framework for developing applications powered by language models
  • Working with external documents and long term memory using vector databases like Pinecone for augmented queries & semantic search
  • Experimenting with autonomous agents like AutoGPT

By the end of this track you'll have a portfolio of notebooks, starter applications, and ChatGPT Plugins that you can use to launch or advance your career in AI.


This track is divided into eight sections that will guide you through beginner and advanced topics, including:

  1. Introduction to Large Language Models and Generative AI
  2. Introduction to Prompt Engineering
  3. GPT-3 Fine-Tuning & Embeddings
  4. Building Applications with GPT-3 & Whisper
  5. Building Applications with ChatGPT & GPT-4
  6. Building Applications with LangChain & Pinecone
  7. Building ChatGPT Plugins
  8. Exploring Autonomous Agents like AutoGPT

While this isn't a "course" in the traditional sense, each article and video tutorial in the track is meant to be a standalone guide to help you build essential skills and master this rapidly advancing field.

Note that our premium tutorials assume you have some familiarity with Python, although we do provide all the necessary code and have plenty of free tutorials to help you get started.

Whether you're looking to build AI assistants to augment your existing work, launch a new LLM-enabled app, or just stay up-to-date with the latest advances in AI, we hope these guides help you in your journey to becoming a highly-skilled AI practitioner.


Section 1: Introduction to LLMs & Generative AI

  • What is a Large Language Model (LLM)?
  • What is Generative AI? Key Concepts & Use Cases
  • What is ChatGPT? Key Concepts & Use Cases
What is a Large Language Model (LLM)?
In this guide, we’ll discuss everything you need to know about Large Language Models (LLMs), including key terms, algorithms, fine-tuning, and more.
What is Generative AI? Key Concepts & Use Cases
In this article, we’ll discuss key concepts about generative AI, including what it is, generative AI models, generative AI startups to watch, and more.
What is ChatGPT? Key Concepts & Use Cases
In this guide, we’ll review the chatbot everyone on the internet is talking about: ChatGPT. We’ll discuss what ChatGPT is, its limitations, key concepts, use cases, and more.

Section 2: Introduction to Prompt Engineering

  • Introduction to Prompt Engineering: Key Concepts & Use Cases
  • Prompt Engineering: Improving Responses & Reliability
  • Prompt Engineering: Advanced Techniques
Introduction to Prompt Engineering: Key Concepts & Use Cases
In this guide, we’ll discuss prompt engineering, which involves the skillful design or input prompts to large language models (LLMs) to improve their performance.
Prompt Engineering: Improving Responses & Reliability
In this guide, we’ll discuss several prompt engineering techniques and best practices to improve GPT-3 and GPT-4 responses and reliability.
Prompt Engineering: Advanced Techniques
In this guide, we review several advanced prompt engineering techniques, including chain of shought (CoT) prompting, self consistency, ReAct, and more.

Section 3: GPT-3 Fine-Tuning & Embeddings

  • GPT-3 Fine Tuning: Key Concepts & Use Cases
  • Making Recommendations Using GPT-3 & Embeddings
  • Building a Custom GPT-3 Q&A Bot Using Embeddings
GPT-3 Fine Tuning: Key Concepts & Use Cases
In this guide, we discuss what GPT-3 fine-tuning is, including key concepts such as how to prepare a fine-tuning dataset, use cases, and more.
Building a Custom GPT-3 Q&A Bot Using Embeddings
In this guide, we discuss how to fine-tune GPT-3 to create a factual question-and-answer bot based on additional knowledge.
Fine Tuning GPT-3: Making Recommendations Using Embeddings
In this guide, we discuss how to fine tune GPT-3 with OpenAI’s Embeddings API and nearest neighbor search in order to build a recommendation engine.

Section 4: Building Applications with GPT-3

  • OpenAI Whisper & GPT-3: Summarizing YouTube Videos
  • MLQ Academy: Building a YouTube Video Assistant Using GPT-3 & Whisper
  • MLQ Academy: Create a Custom Q&A Bot with GPT-3 & Embeddings
  • MLQ Academy: PDF to Q&A Assistant Using Embeddings & GPT-3
  • MLQ Academy: Buidling a GPT-3 Enabled App with Streamlit
  • MLQ Academy: Building an Earnings Call Assistant
OpenAI Whisper + GPT-3 Fine Tuning: Summarizing YouTube Videos
In this article, we discuss how to combine OpenAI’s Whisper and GPT-3 fine tuning in order to summarize a YouTube video.
MLQ Academy: Build a YouTube Video Assistant Using Whisper & GPT-3
In this video tutorial, we’ll walk through a Colab notebook on how to use Whisper and GPT-3 to build a YouTube assistant that can transcribe videos and allow users to ask questions about them.
MLQ Academy: Create a Custom Q&A Bot with GPT-3 & Embeddings
In this video tutorial, we’ll walk through a Colab notebook that demonstrates how to create a factual Q&A assistant using the OpenAI Embeddings & Completions API.
MLQ Academy: PDF to Q&A Assistant using Embeddings & GPT-3
In this video tutorial, we’ll walk through a Colab notebook that shows you how to upload a PDF document and create a factual Q&A assistant based on the text using GPT-3.
MLQ Academy: Building a GPT-3 Enabled App with Streamlit
In this video tutorial, we’ll be creating a simple web app using Streamlit that allows users to upload a PDF, input a YouTube video, or retrieve an earnings call transcript, and compute the embeddings of the document or video.
MLQ Academy: Build a GPT-3 Enabled Earnings Call Assistant
In this video tutorial, we’ll walk through how to create an earnings call assistant using OpenAI Embeddings and Completions API.

Section 5: Building Applications with ChatGPT & GPT-4

  • ChatGPT API: Getting Started with OpenAI’s New Model
  • GPT-4 & Pinecone: Turning Any Website into an AI Assistant
  • GPT-4 & Pinecone - Ask Questions About Any Website: MLQ Academy
  • GPT-4 & Streamlit - Building a Frontend for an AI/ML Tutor: MLQ Academy
ChatGPT API: Getting Started with OpenAI’s New Model
In this article, we’ll discuss how to get started with the ChatGPT API, including migrating from previous models, best practices, and more.
MLQ Academy: Getting Started with the ChatGPT API
In this video tutorial we’ll walk through how to get started with the ChatGPT API, including how to make your first API request, best practices, and more.
GPT-4 & Pinecone: Turning Any Website into an AI Assistant
In this guide, we’re going to augment GPT-4 with a separate body of knowledge and use a vector database to create a custom AI assistant.
GPT-4 & Pinecone - Ask Questions About Any Website: MLQ Academy
In this video tutorial, we’re walk through a Colab notebook that shows you how to augment GPT-4 with a separate body of knowledge to create a custom AI assistant.
GPT-4 & Streamlit - Building a Frontend for an AI/ML Tutor: MLQ Academy
In this video tutorial, we’ll build a simple frontend for an AI/ML tutor using GPT-4, Streamlit, and Pinecone.

Section 6: Developing Applications with LangChain & Pinecone

  • Prompt Engineering: Getting Started with LangChain
  • Getting Started with LangChain: MLQ Academy
  • Building a GPT-3 Enabled Document Assistant with LangChain
  • Building a GPT-3 Enabled Research Assistant with LangChain & Pinecone
  • Building a GPT-3 Enabled Document Assistant with LangChain: MLQ Academy
  • Building an AI Research Assistant with LangChain & Pinecone: MLQ Academy
Prompt Engineering: Getting Started with LangChain
In this prompt engineering guide, we’ll discuss how to get started with a powerful library for building more complex LLM applications: LangChain.
Getting Started with LangChain: MLQ Academy
In this video tutorial, we’ll walk through how to get started with a powerful library for building more advanced LLM-enabled applications: LangChain.
Building a GPT-3 Enabled Document Assistant with LangChain
In this guide, we look at how to build a GPT-3 enabled document assistant using OpenAI and LangChain.
Building a GPT-3 Enabled Research Assistant with LangChain & Pinecone
In this guide, we’ll see how we can use LangChain and Pinecone to build a GPT-3 enabled research assistant that is trained on 50+ machine learning papers.
Building a GPT-3 Enabled Document Assistant with LangChain: MLQ Academy
In this video tutorial, we’ll discuss how to use LangChain and the OpenAI Embeddings in order to upload unstructured documents and be able to ask questions about the document using GPT-3
Building an AI Research Assistant with LangChain & Pinecone: MLQ Academy
In this video tutorial, we’ll discuss how you can use GPT-3, LangChain, and Pinecone to create an AI research assistant.

Section 7: Developing ChatGPT Plugins

  • ChatGPT Plugins: Key Concepts & Use Cases
  • ChatGPT Plugins: How to Build a To-Do List Plugin
  • MLQ Academy: Building a To-Do List Plugin
  • ChatGPT Plugins: Building an AI News Assistant
  • MLQ Academy: AI News Assistant Plugin
  • ChatGPT Plugins: Building a Stock Screener Assistant
  • MLQ Academy: Stock Screener ChatGPT Plugin
  • ChatGPT Plugins: Building an Educational AI/ML Tutor
  • MLQ Academy: Building an AI/ML Tutor
ChatGPT Plugins: Key Concepts & Use Cases
In this guide, we’ll discuss what ChatGPT plugins are, possible use cases of plugins, and why many are calling this the “App Store moment of AI”.
ChatGPT Plugins: How to Build a To-Do List Plugin
In this guide, we’ll see how we can get started building ChatGPT plugins and build a simple to-do list plugin.
ChatGPT Plugins - Building a To-Do List: MLQ Academy
In this video tutorial, we’ll walk through how to build your first ChatGPT Plugin and create a simple to-do list, including building an API, documenting the API, and creating a manifest file.
ChatGPT Plugins: Building an AI News Assistant
In this guide, we’ll walk through how to build a ChatGPT Plugin news assistant that retrieves AI-related news based on the user’s query.
ChatGPT Plugins: AI News Assistant - MLQ Academy
In this video tutorial, we’ll walk through how to build a ChatGPT Plugin that retrieves & summarizes AI-related news.
ChatGPT Plugins: Building a Stock Screener Assistant
In this guide, we’ll walk through how to build a ChatGPT Plugin stock screener assistant using the Financial Modeling Prep API.
ChatGPT Plugins: Stock Screener Assistant - MLQ Academy
In this video tutorial, we’ll build a stock screener assistant using the Financial Modeling Prep API to retrieve and filter stocks based on the user’s input.
ChatGPT Plugins: Building an Educational AI/ML Tutor
In this guide, we’ll build a simple ChatGPT Plugin that guides students through an educational track, which includes lessons, questions, and feedback for an interactive learning experience.
ChatGPT Plugins: Building an AI/ML Tutor - MLQ Academy
In this video tutorial, we’ll walk through how to build a ChatGPT plugin that acts as an AI/ML tutor and guides users down an educational track.

Section 8: Exploring Autonomous Systems

  • Getting Started with Auto-GPT: an Autonomous GPT-4 Experiment
  • MLQ Academy: Getting Started with AutoGPT
  • Adding Long Term Memory to AutoGPT with Pinecone
  • AutoGPT & LangChain: Building an Automated Research Assistant
  • AutoGPT & LangChain: Building an Automated Research Assistant - MLQ Academy
Getting Started with Auto-GPT: an Autonomous GPT-4 Experiment
In this guide, we’ll discuss how to get started with Auto-GPT, the autonomous GPT-4 experiment taking the AI world by storm.
Getting Started with AutoGPT: MLQ Academy
In this video tutorial, we’ll walk through how to get started with AutoGPT: the autonomous GPT-4 experiment taking the AI world by storm.
Adding Long Term Memory to AutoGPT with Pinecone
In this guide, we’ll look at how to add long term memory with Pinecone to AutoGPT, the experimental GPT-4 project taking the AI world by storm.
AutoGPT & LangChain: Building an Automated Research Assistant
In this guide, we’ll walk through how to build an implementation of AutoGPT using LangChain and LLM primitives.
AutoGPT & LangChain: Building an Automated Research Assistant - MLQ Academy
In this video tutorial, we’ll walk through how to build an implementation of AutoGPT using LangChain.

That's it for now! We'll continue to add more guides to the prompt engineering track to help you stay up-to-date with this rapidly advancing field.

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