Module 1: Core Theory
Prompt Engineering Fundamentals
Understanding the Science of Talking to AI
What is Prompt Engineering?
Prompt Engineering is the art and science of structuring text to effectively communicate with and guide Large Language Models (LLMs) to generate the desired output.
It's not just 'asking questions'. It is a form of programming in natural language. Just as a Python developer writes code to execute logic, a Prompt Engineer writes context and constraints to navigate the probabilistic latent space of an AI model.
The Chef Analogy
Think of an LLM as a Michelin-star chef (the diverse training data). The Prompt is the order ticket. If you write 'Make me dinner', you might get anything from a salad to a steak. If you write 'Prepare a medium-rare Wagyu ribeye with truffle mash, presented on a warm plate', you get excellence. Specificity is the currency of quality.
The 4 Core Pillars
Every successful prompt is built on these four pillars. Missing one often leads to generic or incorrect outputs.
Instructions
The specific task you want the model to perform. (e.g., 'Summarize', 'Translate', 'Code', 'Analyze').
Context
Background information that limits the scope. Who is the persona? What is the situation? (e.g., 'Act as a Senior Legal Consultant...').
Input Data
The actual content to process. (e.g., The text of the email to reply to, or the CSV data to analyze). Always separate this clearly.
Output Indicator
The format of the desired result. (e.g., 'Format as a markdown table with columns: ID, Name, Score').
The CO-STAR Framework
A proven industry standard for structuring complex prompts.
Context
Provide background information on the task.
Objective
Define clearly what you want the AI to do.
Style
Specify the writing style (e.g., Business, Academic, Witty, Journalistic).
Tone
Set the attitude (e.g., Formal, Casual, Empathetic, Authoritative).
Audience
Who is this for? (e.g., 5-year-olds, Experts, CEO).
Response
The output format key (JSON, HTML, List, Email).
Context: I am a software engineer looking for a job. Objective: Write a cover letter. Style: Professional and data-driven. Tone: Confident but humble. Audience: Hiring Manager at Google. Response: A 3-paragraph text block.
Know Your Model
Different models have different 'personalities' and strengths.
Google Gemini
Huge context, strong multimodal (image, audio, video), deep Google ecosystem ties.
Long-document analysis, research with Workspace, multimodal reasoning.
Put durable rules in system instructions; keep task-specific details in the user turn.
ChatGPT / GPT models
Strong reasoning, tool use, coding assistance, and broad ecosystem plugins.
Analysis, coding workflows, creative drafting, agent-style tool loops.
Use custom instructions or projects for lasting context; keep each task prompt scoped.
Claude
Excellent writing, careful coding, long context, and strong instruction following.
Document drafting, codebase edits, nuanced analysis, XML-structured prompts.
Claude responds well to clear XML-ish structure: wrap inputs in tagged blocks.
Under the Hood: Mechanics
Tokens
LLMs don't read words; they read chunks of characters called 'tokens'.
1000 tokens ≈ 750 words. 'Hamburger' is one token. 'Skibidi' might be 3 tokens. This is why LLMs struggle with tasks like 'count the letters in strawberry'—they don't see the letters.
Temperature (0.0 - 1.0)
Controls the randomness of the output.
Low (0.2): Deterministic, focused, analytical. Good for coding and math. High (0.8): Creative, diverse, unpredictable. Good for poetry and brainstorming.
Top-K & Top-P
Advanced sampling parameters.
Limits the pool of next-token choices. Lowering these makes the model 'stick to the script' more rigidly.