The Complete Prompt Engineering for AI Bootcamp (2026)

What are the five principles of prompting, and how can they help you get more reliable and higher quality AI outputs?

Describe the desired style in detail, or reference a relevant persona.

Define what rules to follow, and the required structure of the response.

Insert a diverse set of test cases where the task was done correctly.

Define what good looks like, testing what changes drive performance.

Split tasks into multiple steps, chained together for complex goals.

Work through the five principles checklist template to optimize your prompts.

Explain what Token Limits are and how to get the token limits without and with code.

Learn about what AI hallucinations are, and how you can avoid ChatGPT making mistakes or producing factually incorrect information by grounding ChatGPT with knowledge.

Discover the chat-based model ChatGPT, to generate answers for your questions.

You can now search multiple websites and get citations in your response without hallucinations.

Research topics to extreme depth using many search queries and summarizing the results into a report.

Learn about parallel threads, and how you can multi-task within ChatGPT. This allows you to work more efficiency, especially when you've doing work that has a slow response such as deep research.

ChatGPT allows you to upload files like CSVs, spreadsheets, PDFs, JSON, and text documents so it can ground its reasoning in real data rather than abstractions. Once added, it can analyze, evaluate, and synthesize across those files to produce summaries, insights, transformations, and entirely new artifacts.

Learn how to use ChatGPT to analyse data and how code execution works. This ability allows you to easily analyse .csv, Excel files and many more without having to use more advanced tools such as  Microsoft Excel, Tableau or Python.

Analyze and interpret images with ChatGPT and answer queries about them, expanding beyond text-based applications.

Artifacts are dedicated, interactive workspaces that open in a side panel next to your chat when ChatGPT/Claude generates content like code, documents, or apps.

Learn what is memory and how to use memory within ChatGPT to improve the personalization of your ChatGPT sessions.

You will learn how to use ChatGPT Projects to organize your ideas, files, and conversations into structured workspaces that support deeper focus and execution.

Custom instructions give you more control over how ChatGPT responds. Set your preferences, and ChatGPT will keep them in mind for all future conversations.

Keyboard shortcuts: Work faster with shortcuts, like ⌘ (Ctrl) + Shift + ; to copy last code block. Try ⌘ (Ctrl) + / to see the complete list.

Learn about ChatGPT's scheduled tasks feature - how to automate your work by creating recurring AI-powered tasks that can run independently and notify you when complete. This lesson covers setting up tasks, managing notifications across devices, and understanding usage limitations in the beta release.

Discover ChatGPT Agent Mode, OpenAI's groundbreaking feature that enables ChatGPT to autonomously complete complex tasks using its own virtual computer and browser, seamlessly combining web browsing, code execution, and document creation capabilities.

Learn about what skills are, how they work inside of ChatGPT and Claude. Skills were originally created by Anthropic's team and use a technique called progress disclosure. You'll learn how to write, edit, update and delete skills inside of ChatGPT.

Easily understand what Model Context Protocol is, how it works and what you can do with the equivalent inside of ChatGPT (also known as plugins).

This will allow you to integrate ChatGPT and equivalent platforms into your external systems and daily tools including Google Drive, Google Mail and many more!

Now that you understand what skills and MCP are, you'll learn that it is possible to create your own MCP servers. In this lesson you'll connect to a custom MCP server hosted on understandingdata.com. This MCP server demonstrates a 'book store', and how you can connect your own systems directly into ChatGPT.

For future reading/implementation, it's recommended to ask Codex/Claude code on how to implement a custom MCP server for your use case.

Learn how to install and use the ChatGPT desktop application for either Mac or Windows with voice chat and screenshot capabilities.

Computer use allows ChatGPT to programmatically control your computer, you'll learn how this is different to agent mode and when you should best use it.

The main use case for computer use is when the program that you want to automate doesn't offer an API (application programming interface) and clicking within the user interface is the only tactic remaining.

An introduction to how image models turn your words into pictures, and a map of the sixteen techniques in this section, from style and lighting modifiers to consistent characters and brand templates.

You also get two free companion tools: the Image Prompting Technique Lab, with every prompt and result from the course, and the Image Style Explorer, with 96 art styles you can compare and turn into prompts.

Create images for blogs, presentations, invitations, wall art, and stories, then transform uploaded photos and refine results with follow-up prompts.

Apply artistic styles to your images and switch between mediums, from realistic product photography to watercolor illustration.

Change lighting, color palettes, camera angles, and depth of field to control an image’s mood and composition.

Ask ChatGPT to write an image prompt, adjust its creative choices, and test the results.

Brainstorm visual ways to communicate an abstract idea, then generate and compare interpretations for a blog or presentation.

Break an artistic style into specific visual instructions so you can adapt individual elements and create your own interpretation.

Combine the colors, textures, and composition of different reference images to create a new visual direction.

Find the shared visual characteristics of images you like and turn them into useful style names and prompt vocabulary.

Turn a rough sketch into a finished illustration or realistic image, then refine details with follow-up instructions.

Use a reference image to guide a new image’s pose and composition while changing the person or subject.

Make targeted edits using text instructions, comments, and markup, including changing colors, removing objects, and removing backgrounds.

Generate four variations in one request and compare them to choose the composition or creative direction you prefer.

Use a reference image to maintain a person’s recognizable appearance across different scenes and outfits.

Place a product into lifestyle and studio scenes while preserving its packaging and visual identity.

Use a generated alphanumeric string as creative inspiration to explore different palettes, settings, materials, and compositions.

Build a reusable prompt that maintains a consistent visual style while letting you change the subject or scene.

A free companion lab for this section that works with any video model. It covers 19 techniques with before and after clips, a prompt checker and builder for the ten parts of a video prompt, and a 3D camera vocabulary of 79 shots, moves, lenses and lighting terms. You can also practise diagnosing real clips that went wrong and choose the one change that would fix each.

Discover the state-of-the-art video model Google Veo3, the first model that’s actually usable

Veo3 is the revolutionary text to image model from Google, able to make realistic cuts or animated scenes better than any other model.

If you want more control over your prompts to Veo3 it actually understands JSON, so you can more closely define what specifically you want to happen and when, with scenes, characters, camera angles, and audio.

Instead of limiting yourself to 8 second shots, prompt multiple cuts in a single generation

Take a starting and/or ending frame and generate the full video based on those images, keeping character and scene consistency.

Build a video clip based on multiple elements to combine scenes, characters and other ingredients as needed rather than relying on frames.

Google Veo3 actually understands what it's seeing in the image you upload to it, so you can draw on that image to give art direction to the model in terms of what should be animated and where.

This video introduces all of the different tactics/techniques you'll learn for improving outputs when using Generative AI tooling. These tactics span across multiple modalities such as text, images and video.

Learn how generative AI models such as ChatGPT and Claude use conversational context and memory, including ChatGPT Memory and other forms of stored context. You’ll also learn how the way you phrase a question can influence an AI model’s response, and how to ask more neutral, less biased questions to get more reliable answers.

Certain tasks are most effectively detailed in a step-by-step manner. By clearly listing the steps, the model's ability to adhere to them can be enhanced.

Explore the concept of meta prompting, where you learn to craft prompts based on desired outputs, enabling you to generate more targeted and relevant AI-generated content by reverse-engineering the input-output relationship.

Discover how to simplify complex topics using ChatGPT, making them accessible and easy to understand for individuals of all ages, especially for those new to a subject or concept.

Explore the concept of role prompting, understanding how to enhance AI-generated content by assigning specific roles or perspectives to the model, resulting in more engaging and contextually relevant outputs.

Learn how to request context from ChatGPT, enabling you to generate more accurate and relevant AI-generated content by providing the necessary background information and ensuring a better understanding of the topic at hand.

Learn about key phrases that dramatically change the behaviour of large language models. These key phrases include:

- Temporal (work for X hours)

- Objectives (work until an objective is completed)

And many more.

Learn about how to manipulate data within ChatGPT to transform from and to different types of files. This lesson talks about how you can create PowerPoint presentations directly from text, and how text can be transformed into a .csv for easier data analysis tasks.

Learn how to bypass the token output limitations of ChatGPT and other Large Language Models by breaking your content generation into strategic steps. This effective technique allows you to create comprehensive, high-quality content that exceeds standard token limits while maintaining coherence and flow throughout your projects.

Master the least to most problem-solving approach, where you learn to decompose complex tasks into subproblems and sequentially solve each one, resulting in a more efficient and effective method for tackling challenging situations.

In this lesson you'll understand what Codex is, why it's different from ChatGPT and when to leverage ChatGPT vs Codex. You'll also explore what /goal is, when you should use it.

In this lesson you'll learn about the benefits of using adversarial review, how to set up a reviewer/sub-agent that reviews the output of an existing AI model.

Subagents and adversarial review are some of the more advanced but powerful techniques when using AI models.

Prepare the ground for ChatGPT to do good work, by asking it to give itself advice.

This video explains how to access the coding course content using either Google Colab or GitHub.

If you’re new to programming, I recommend following along using Google Colab, as it lets you run the code directly in your browser without setting up a local development environment.

If you’re already a developer, I recommend cloning the GitHub repository and running the code locally.

Learn about the essential capabilities of OpenAI's API including text generation, structured outputs, image analysis and generation, speech conversion, function calling, reasoning models, and embeddings. Discover how these powerful features can be leveraged to build intelligent applications across various use cases from semantic search to knowledge bases.

This comprehensive overview will prepare you to harness the full potential of OpenAI's technology suite in your development projects.

Discover how to set up your OpenAI developer account, create an API key, and configure billing so you can start using the platform's powerful capabilities. This step-by-step guide walks you through the essential account setup process, from creating your profile to securing your API credentials.

Learn important security best practices to protect your account and prepare for hands-on exploration of the OpenAI playground in subsequent lessons.

Discover how to use OpenAI's powerful playground interface to experiment with different models, tools, and parameters without writing code. This interactive environment allows you to test prompts, compare model outputs, and generate structured data formats while saving your configurations for future use.

Learn to implement OpenAI's Responses API with step-by-step guidance on managing conversation history both locally and on OpenAI's servers. This practical tutorial demonstrates how to maintain context across multiple interactions by properly handling message history and using response IDs for seamless conversation continuity. Master these essential techniques to build more natural conversational AI applications with proper state management.

Dive into OpenAI's core API features with this practical walkthrough covering essential capabilities from text generation to embeddings. This comprehensive guide demonstrates how to implement key functionalities including structured outputs, image analysis, text-to-speech conversion, function calling, and numerical text representations using actual code examples.

Learn how to control model parameters and leverage different model types to build powerful AI applications with OpenAI's platform.

Learn to accurately count and manage tokens in your OpenAI API calls using the tiktoken Python package. This practical tutorial demonstrates how to encode text into tokens, calculate token usage for different models, and verify that your estimates match actual OpenAI charges. Master this essential skill to optimize costs, manage token limits, and handle conversation history effectively when working with language models.

Learn how to effectively manage token usage in your OpenAI API calls by implementing custom token counting with the tiktoken package. This practical guide demonstrates how to maintain conversation history within specific token limits by strategically removing older messages while preserving system prompts. Master this essential technique to optimize costs and performance when generating lengthy AI responses across multiple conversation turns.

Learn how to implement OpenAI's streaming capability to receive real-time responses rather than waiting for complete outputs. This practical tutorial demonstrates how to enable streaming with a simple parameter change and process incoming event types to build dynamic, responsive AI applications.

Master this essential technique to create more interactive user experiences by handling response deltas as they arrive instead of waiting for complete model outputs.

In this practical lesson, you'll master essential techniques for handling OpenAI API rate limits with Python, learning how to implement smart retry strategies that keep your applications running smoothly even under heavy usage. You'll discover multiple implementation approaches including client customization, the Tenacity package, and manual exponential backoff, plus gain valuable insights into monitoring your usage and leveraging the Batch API for high-volume workload

Learn about the difference between the Chat Completions API end point vs the Responses API end point.

Structured outputs turn model replies into guaranteed, schema-validated JSON instead of messy free text. You define the structure, and the model fills it in correctly, so your app gets clean, typed data without fragile parsing.

Learn how to easily extract structured data from text via OpenAI’s structured output API.

Tool calling lets your model use real functions in your system instead of just replying with text. You define the tools, the model decides when to call them, and your app runs the code and returns the result.

In this lesson, you'll get hands-on with Python code to implement tool calling, allowing you to create powerful applications where language models can interact with your custom functions. You'll build a real weather lookup example from scratch, and gain the essential skills to start creating your own tool-enabled AI applications that make smart decisions about when to use the tools you provide.

In this hands-on lesson, you'll build your own AI agent from scratch using Python, creating a system that can intelligently decide when to use tools and when to respond directly to users. You'll implement the essential "agentic loop" pattern that powers modern AI assistants, customize your agent to handle multiple weather queries across different cities, and learn practical techniques to control your agent's behavior with custom objective functions.

Make many LLM calls at the same time through parallelization.

In this lesson you'll learn about remote code execution: letting the model write and run Python on OpenAI's servers instead of your own machine, which keeps untrusted code isolated, gives the model real computation instead of guesswork, and means you have nothing to install or secure locally.

You'll then use OpenAI's Code Interpreter tool to upload a file, have the model analyse it and build a chart inside a sandboxed container, and download the results to check them yourself.

In this lesson you'll learn how computer use lets a model operate a browser or desktop the way a person would, by looking at screenshots and choosing clicks and keystrokes, which means it can automate tasks in apps that have no API. You'll then use OpenAI's computer use tool to fill in a form in a safe, isolated test browser, with your own Python code carrying out each action and sending the new screenshot back to the model.

In this lesson you'll learn how managed agents let OpenAI run the whole agent loop for you, handling sessions, tool calls and code execution in a hosted environment so you don't have to build or host that machinery yourself.

You'll then use OpenAI's Agents API to create a reusable agent, give it a multi-step task, send a follow-up in the same session and download the files it produces so you can check the results.

You will explore and compare six retrieval methods, from typo-tolerant matching and TF-IDF to embeddings, a toy neural ranker, and hybrid search. You will learn when each one works, when it fails, and why mixing keyword plus semantic signals usually wins.

In this hands-on lesson, you will master the fundamentals of embeddings - the numerical representations that power modern AI applications. You'll learn how embeddings capture semantic meaning in high-dimensional vector spaces, and gain practical experience generating, visualizing, and comparing embeddings using OpenAI's API through interactive coding exercises.

You will build a full RAG pipeline end to end: chunk a document, embed and index it with FAISS, then retrieve the best chunks for a question and feed them into the model as grounded context. You will also test what breaks when you skip retrieval, so you can see exactly how RAG reduces hallucinations and keeps answers tied to your own data.

In this lesson, you will learn why modern AI systems use hybrid retrieval and how combining methods like vector search, keyword search, and graph retrieval improves accuracy and reasoning. By the end, you will be able to explain the benefits, tradeoffs, and practical architectures of hybrid retrieval clearly and concisely.

You will learn how to measure retrieval quality for RAG by computing Precision@K, Recall@K, MRR, and NDCG step by step on a toy dataset. By the end, you will know what each metric actually tells you, how rankings affect them, and how to use these scores to spot and fix weak retrieval.

We've included extra Jupyter notebooks for advanced retrieval patterns, if you're interested in learning more. Please visit find the notebooks in the advanced_retrieval_techniques folder within the Github repository.

In this lesson, you will identify the key differences between AI workflows and AI agents, and understand when each approach is appropriate. You will analyze common orchestration patterns such as sequential chains, routing, and DAG pipelines, and compare them to agent reasoning loops that dynamically select tools and actions. By the end, you will be able to evaluate whether a problem is better solved with a deterministic workflow, an autonomous agent, or a hybrid approach.

Build an agent that retrieves answers from multiple sources including SQLite databases and text documents. You’ll learn how to design grounded retrieval pipelines, rank results, enforce citations, and prevent hallucinations. This lesson teaches real-world RAG architecture, not just prompting.

This lesson explores how you can build and run AI agents using the OpenAI Agents SDK. You create, configure, and execute agents that use tools, structured outputs, sub-agents, and web search without writing manual tool-dispatch loops. By the end, you apply these concepts to build your own agent with tools and structured outputs, gaining hands-on experience with modern agent orchestration.

Design a self-correcting content agent using a planner, executor, and critic architecture. You’ll implement bounded iteration, quality scoring, and refinement loops to improve output over multiple passes. This lesson demonstrates orchestration patterns used in robust multi-agent systems.

Build a complete coding agent from first principles by progressively adding core capabilities like reading files, exploring directories, running commands, editing code, searching patterns, and integrating live documentation via MCP. Understand how these simple tools compose into a powerful agent loop, enabling autonomous code exploration, modification, and verification in real workflows.