Unlock the Power of AI with Our Prompt Engineering Full Course

This foundational chapter demystifies Large Language Models (LLMs). An LLM is a neural network trained on vast text data, allowing it to understand grammar, context, and reasoning. We distinguish between a **Base LLM**, which predicts the next word, and an **Instruction-Tuned LLM**, which is fine-tuned to follow user commands, making it ideal for applications. Key capabilities like summarizing, inferring, transforming, and expanding text are introduced.

This chapter focuses on two core principles: writing **clear and specific instructions** and giving the model **time to "think."** Clarity is achieved by using delimiters, asking for structured output (e.g., JSON), and asking the model to check conditions. Giving the model time to think involves instructing it to work out steps before providing an answer, which improves accuracy on complex tasks.

Great prompts are rarely written on the first try. This section teaches a systematic workflow: start with a simple idea, analyze the output for errors, refine the instructions based on those errors, and repeat. This structured approach is essential for developing robust and reliable prompts suitable for production environments.

Learn to generate concise summaries of long documents. We explore practical applications, such as extracting key information from product reviews, news articles, or technical papers, with options to control the length and focus of the summary.

This topic covers using an LLM to extract information that is not explicitly stated. You'll learn to perform sentiment analysis, identify key topics in a document, and extract structured data like names or companies from unstructured text.

Text transformation involves converting text from one format, style, or language to another. This module covers a range of tasks, including language translation, tone adjustment (e.g., formal to informal), and format conversion (e.g., JSON to HTML).

The opposite of summarizing, expanding text involves generating longer, more detailed content from a small piece of input. Learn to use an LLM to write personalized emails, create detailed product descriptions from a few keywords, or elaborate on a topic for a report.

Learn the mechanics of building a conversational agent. This section covers the essential role of **"System" messages** to define the chatbot's persona, rules, and objectives. You'll work with the chat completions API, managing the conversational history to maintain context and create a natural user experience.

This chapter provides a comprehensive review of the key principles and techniques covered throughout the course. We'll consolidate your understanding of iterative prompt development and best practices. Additionally, this module serves as a valuable resource hub, pointing you towards further reading, advanced tools, and communities to continue your learning journey in the rapidly evolving field of prompt engineering.

This module provides a formal definition of prompt engineering as the practice of designing inputs for AI models to produce desired outputs. It positions prompting as a new kind of software development, where natural language is the code. You'll understand its significance in controlling generative AI and why it has become a critical skill for developers, writers, and professionals across various fields. The history and evolution of prompting are also discussed, providing a solid theoretical foundation.

Learn the fundamental building blocks of effective prompts. This section introduces foundational patterns, including the **Persona Pattern**, where you assign a role to the AI (e.g., "You are an expert copywriter"). It also covers patterns for question refinement, audience adaptation, and defining the output format explicitly, which are essential for gaining reliable results from the AI.

Move beyond the basics to explore more complex techniques. This includes providing high-quality context, defining complex output structures (like nested JSON), combining multiple prompt patterns into a single sophisticated prompt, and using **meta-prompts**—a technique where you ask the AI to generate or critique a prompt for a specific task, leveraging the AI's own knowledge to improve your prompts.

This module covers two fundamental interaction paradigms. **Zero-Shot Learning** relies on the model's vast pre-existing knowledge to perform a task from an instruction alone. **Few-Shot Learning** enhances this by providing a few examples ("shots") of the task within the prompt itself, guiding the model on the expected format, style, or logic. You'll learn when to use each approach to maximize performance.

A deep dive into **Chain of Thought (CoT)**, a powerful technique that improves an AI's reasoning capabilities. By instructing the model to "think step-by-step," you encourage it to break down complex problems into intermediate steps before giving a final answer, dramatically improving performance on arithmetic, commonsense, and symbolic reasoning tasks. This section also explores variations like Zero-Shot CoT.

Apply your knowledge through practical projects and the analysis of real-world case studies. This module shows how prompt engineering is used across various industries, from marketing and content creation to software development and data analysis. These hands-on examples solidify your understanding and prepare you to tackle your own prompt engineering challenges.

Anthropic emphasizes that being explicit is the most critical element for getting high-quality outputs from Claude. This chapter teaches you to provide detailed context, define the desired audience, specify the tone, and outline the format precisely, leaving no room for misinterpretation.

Explore how to structure prompts to ensure the model follows complex, multi-part instructions. This includes techniques for ordering instructions logically and using formatting to create a clear visual hierarchy for the model to interpret.

Claude models are specifically fine-tuned to recognize XML tags. This section teaches you to use tags like `` and `` to clearly separate different parts of the prompt, a highly effective technique for complex tasks.

Learn to break down highly complex tasks into a sequence of smaller, more manageable prompts. This "chaining" technique, where the output of one prompt becomes the input for the next, improves reliability and makes debugging easier.

Provide examples of desired input-output pairs within your prompt to guide Claude on the specific format, style, and logic you require. This is especially useful for tasks that are difficult to describe with instructions alone.

Learn strategies to reduce the likelihood of the model generating factually incorrect information. Techniques include providing relevant documents as context and asking the model to cite its sources from the provided text.

Understand Anthropic's safety-first approach and learn how to write prompts that avoid generating harmful, unethical, or biased content, aligning the AI's output with responsible and ethical guidelines.

When a prompt doesn't work, this module teaches you how to systematically identify the failure point. Learn to isolate variables, test parts of the prompt in isolation, and simplify the task to find out why the AI is failing to perform as expected.

Explore practical applications and see how prompt engineering techniques are applied in professional contexts like legal document analysis, financial report summarization, and extracting medical information from patient notes.