Module 1: Foundations of Prompt Engineering

Module Overview¶

Welcome to Module 1 of the Prompt Engineering curriculum! This module establishes the groundwork for our comprehensive journey. You’ll grasp not only what prompt engineering entails but also its significance—how the phrasing, structure, and precision of prompts influence the quality and applicability of AI outputs in fields like research, business, and creativity. We’ll also lay the foundation for understanding how model training (explored in Module 2) shapes effective prompting.

Our objectives are dual: First, define prompt engineering and trace its evolution as a critical discipline in AI. Second, learn to build, analyze, and refine prompts by examining their structure, essential frameworks, and the mechanics of large language models (LLMs)[2]. This module is designed for self-paced learning, allowing you to progress at your own speed.

Learning Objectives¶

By the end of this module, you should be able to:

Define prompt engineering and articulate its escalating role in AI systems and human-AI collaboration[2].
Identify and explain the core elements of an effective prompt, such as task definition, context, instructions, constraints, and formatting[1].
Understand and apply the RACE Framework—a structured method for crafting prompts[2].
Outline how LLMs handle prompts, including parameters like temperature and top-p, and how these tie to training processes (covered in Module 2)[5].
Recognize basic prompt patterns (e.g., question-answering, instructional, creative writing, and chain-of-thought)[1].

What is Prompt Engineering?¶

Prompt engineering involves designing and optimizing input queries or instructions to elicit the most accurate, relevant, and valuable responses from AI models like LLMs[2]. Its importance has grown with LLMs’ integration into diverse applications, from customer service to scientific research and code generation. By carefully selecting words, providing context, and setting guidelines, users can minimize ambiguity, boost reliability, and foster better human-AI synergy, leveraging insights from model training (see Module 2)[2].

Anatomy of a Prompt¶

Prompts can be dissected into key components1:

Task Definition: Explicitly stating the request (e.g., summarize, classify, generate).
Context: Supplying pertinent background or details to guide the response.
Instructions: Detailed directives on style, length, or approach (e.g., “use bullet points”).
Constraints: Boundaries for the output (e.g., limit to 200 words, avoid certain topics).
Formatting: Specifications for presentation (e.g., “output as JSON”). These elements, when combined effectively, reduce variability, prevent misunderstandings, and align outputs with user intent, building on how models are tuned to interpret instructions[1].

The RACE Framework¶

The RACE framework has become widely adopted in recent prompt engineering practices 2:

Role: Assign a persona to the model (e.g., “Act as a historian”).
Action: Define the specific task or goal.
Context: Provide necessary background information.
Expectation: Clarify the desired format, style, or output details.

This framework builds on earlier ones like CLEAR by emphasizing role-playing for enhanced specificity, helping users fill prompt gaps systematically and improve LLM performance across tasks, especially when aligned with instruction tuning (Module 2)[2].

How Large Language Models Process Prompts¶

LLMs tokenize inputs—breaking text into subword units—then use deep neural architectures (typically transformer-based) to predict and generate the next tokens to produce responses, shaped by their training (see Module 2)[5]. Parameters like temperature (controlling randomness: low for predictability, high for creativity) and top-p (nucleus sampling: restricting to probable tokens) influence variability. In 2025, with advanced models, understanding these allows fine-tuning for tasks, balancing determinism and innovation[5].

Common Prompt Patterns¶

Established patterns in prompt engineering include1:

Question-Answering: Focused queries with format specs.
Instructional: Command-based (e.g., “Analyze this data”).
Creative Writing: Open prompts, e.g., “Invent a story about AI ethics.”
Chain-of-Thought: Encouraging step-by-step reasoning for complex problems, though results should be critically evaluated, as reasoning traces do not always ensure correctness.

Selecting appropriate patterns enhances accuracy, relevance, or creativity, forming a core skill in modern prompt engineering, especially when leveraging tuned models (Module 2)[1].

Suggested Activities (Optional)¶

Examine examples from readings and videos of strong vs. weak prompts; note observed differences.
Craft a prompt for a personal topic (e.g., recipe generation) and tweak its context or constraints to see output variations.
Reframe a prompt using the RACE framework and compare model outputs with a simpler, less-structured version.

Syllabus