The Complete Prompt Engineering for AI Bootcamp (2026)

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

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.

Identify errors and rate responses, testing what drives 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.

This lesson showcases OpenAI's new GPT-5 reasoning models (GPT-5 with Thinking).

Reasoning models are designed for deep analytical thinking, with enhanced capabilities in science, coding, and mathematical problem-solving. This lesson covers the differences between chat models (GPT-5-fast) and reasoning models (GPT-5 with Thinking).

Also we discuss when to use each model and their various trade-offs.

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.

Write your first prompt for ChatGPT, and figure out how the interface works.

Discuss what ChatGPT can and can't do, to determine if it's suitable for your project.

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 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.

Understand how to easily create images within ChatGPT. Learn how to use reference images to create similar images to your original image but in a different style/perspective.

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.

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

Learn how to activate and control this unified agentic system that can handle everything from analyzing competitors and creating slide decks to planning events and updating financial models, while maintaining full user control through permission requests and real-time intervention options.

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 OpenAI's canvas, a groundbreaking new interface that transforms how users collaborate with ChatGPT on writing and coding projects. You'll discover how canvas enables side-by-side creation with AI, featuring powerful shortcuts for editing, length adjustment, and code debugging, making it a valuable tool for both developers and content creators.

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.

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.

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

How to prompt a vision model, and what they are capable of doing

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

Use ChatGPT Atlas to bring smart help directly into your browser so you can summarise pages, rewrite text, and complete tasks without switching tabs.

You will be able to apply AI assistance responsibly while controlling what is remembered, what is shared, and where it operates.

Learn how to use OpenAI's Study Mode in ChatGPT, which transforms the AI from an answer-giving tool into an interactive learning coach that guides you through problems step-by-step using Socratic questioning and personalized scaffolding.

ChatGPT Group Chats let multiple people collaborate in a shared conversation with a single, persistent AI context. The model can track discussion threads, synthesize viewpoints, and help groups analyze, decide, and create together more effectively than isolated one-to-one chats.

Participate in OpenAI’s “app store” moment and build your own custom GPT with actions and tailored prompts.

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 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.

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.

Discover how to simplify complex topics using GPT-3, 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 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.

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.

Learn how to perform sentiment analysis, enhancing your understanding of text data and enabling better decision-making based on the emotions and opinions expressed in the content.

To ensure a highly pertinent response, it's crucial to include any significant details or context in your requests. If these elements are absent, you're essentially allowing the model to infer your intentions, which may lead to less accurate results.

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.

Symbols such as triple quotes, HTML elements, chapter headings, and others serve as separators to distinguish various segments of text that should be interpreted in unique ways.

You have the option to request the model to generate outputs that match a predetermined length. This desired length can be measured in units such as words, sentences, paragraphs, or bullet points. Nonetheless, it's important to understand that guiding the model to produce an exact word count might not yield precise results. Conversely, the model can more dependably produce outputs containing a certain number of paragraphs or bullet points.

Learn how to request context from GPT-3/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.

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

Discover how to overcome token limitations in ChatGPT by chunking text, allowing you to process larger amounts of data more efficiently and effectively while maintaining the integrity of the information being analyzed.

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, adjust temperature settings for creativity control, and generate structured data formats while saving your configurations for future use.

Master this essential development tool to rapidly prototype AI applications and refine your prompts before implementing them in production.

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.

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.

Quick primer on what supabase is and why we use it as a postgreSQL database (and for vectors!)

You will build a production style RAG pipeline using Supabase pgvector, from generating embeddings to storing and querying vectors with cosine similarity in SQL. By the end, you will retrieve grounded context from your own database and feed it into an LLM so answers stay accurate, controlled, and tied to your 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.

This lesson introduces role prompting, a technique where you assign the model a specific expert persona to guide its reasoning and responses. You will learn how defining roles activates relevant knowledge patterns in the model, leading to more focused, domain-aware outputs. By using role prompting effectively, you can steer LLM behaviour to produce clearer explanations, stronger analysis, and more reliable results.

This lesson introduces few-shot prompting and in-context learning, showing how language models can learn patterns from examples included directly in the prompt. You will learn how adding one-shot, two-shot, and three-shot examples can steer outputs more effectively than zero-shot prompting alone, and how to evaluate that improvement systematically.

Learn how emotion prompting uses motivational or urgency-based cues to influence how language models respond to tasks. Discover how emotional stimuli can steer model behaviour and improve completeness, accuracy, and overall output quality.

Understand chain-of-thought prompting, a technique that encourages language models to reason through problems step by step before producing an answer. Learn how prompting models to break down tasks into intermediate reasoning steps can significantly improve performance on complex problems such as math, logic, and multi-step reasoning tasks.

Generate multiple responses, then choose the most popular answer.

Simulate an agent with your AI model, to handle decision-making and tool use.

Explore personas of thought, a prompting technique that asks a language model to simulate multiple relevant perspectives before synthesizing a final answer. Learn how generating and aggregating viewpoints from diverse personas can produce more nuanced, human, and insight-rich responses than a single direct prompt.

Learn how to run prompt A/B tests by generating multiple outputs, collecting human feedback, and comparing variants with simple scoring. See how structured evaluation turns prompt design from guesswork into a measurable, experiment-driven process.

Prompt caching optimizes costs by allowing developers to reuse repeated input text (like instructions or context) sent to AI models at a steep discount, while output tokens remain at full price - making it especially valuable for applications that repeatedly send the same large chunks of context but expect different responses.

Learn how to check prompt caching results on OpenAI calls and also how to manually perform prompt caching within Anthropic.

Explore the OpenAI Realtime Console, an interactive tool that helps you understand how to implement voice conversations and function calling in your applications.

LangChain is a cutting-edge framework designed for crafting applications driven by language models. It seamlessly integrates with data sources, allowing the language model to actively engage with its environment. With its modular components and pre-built chains, users can easily initiate projects or tailor solutions to suit intricate needs.

Learn several different approaches to installing LangChain and also how to expose your OPENAI_API_KEY as an environment variable within Python.

Learn how to load a langchain chat model as well as how to add different types of messages such as SystemMessage, HumanMesssage.

Discover how to create chat prompt templates that'll make your prompts more dynamic.

Learning how to use the streaming parameter in Langchain for reducing latency and obtaining the outputs one token at a time.

Learn how to easily extract structured data from LLM's with Output Parsers.

Discover how to use various summarization techniques including stuffing, MapReduce, and refining to extract meaningful content from large documents. Grasp the importance of each method and how they handle documents differently, ensuring you choose the right strategy for your specific text.

Discover the intricacies of loading documents, splitting texts, and creating LangChain documents. Dive into the world of Beautiful Soup for parsing, manage large texts with recursive text splitters, and maintain the integrity of document chunks with variable overlaps. Learn how to handle large data sources, such as GitHub or markdown files, and how to efficiently break them down for processing with large language models. Emphasize the importance of maintaining content context during the splitting process, and apply MapReduce summarization techniques to efficiently derive meaning from your segmented data.

Dive into the powerful world of tagging with LangChain. Expand your document analysis toolkit to identify and categorize specific features in large datasets. Harness the power of sitemap loaders to retrieve web pages, define JSON schemas to establish tagging criteria, and process content using OpenAI's GPT 3.5 Turbo. Experience seamless integration of structured data with popular Python libraries like pandas and effortlessly enrich your dataset with metadata, such as URLs.

Understand the principles and operation of the LCEL runnable protocol to efficiently execute your AI models.

Understand how to utilize itemgetter and Retrieval Augmented Generation (RAG) techniques to optimize the performance of ChatGPT models.

Understand how to incorporate chat history and memory with LangChain to improve the user engagement and conversation flow.

Construct multiple chains in LangChain, enhancing the flexibility of your AI model's output.

Demonstrate the ability to implement conditional logic, branching and merging to create sophisticated conversational flows in LangChain.

Learn how to effectively structure your document ingestion pipelines with the LangChain Indexing API.

Configurable fields allow you to dynamically change parts of your LCEL runnables at runtime!

Learn about agents, tools and how to create a custom agent with memory in LangChain.