Mastering Prompt Engineering for AI Development

A Comprehensive Guide to Crafting Effective AI Instructions

Learn the art and science of communicating with AI models

8 Modules

Beginner to Advanced

Practical Examples

Module 1: Introduction

Definition and core principles of prompt engineering, evolution of prompting

Module 2: Context Windows

Understanding tokens, managing context effectively

Module 3: Effective Prompts

Minimum viable prompt components, advanced elements

Module 4: Methodologies

The Four S's Framework, iterative prompting

Module 5: Prompting Styles

Zero-shot, one-shot, few-shot, advanced techniques

Module 6: Applications

Development workflows, Retrieval Augmented Generation

Module 7: Model Considerations

Working with different AI models, tool integration

Module 8: Case Studies

Real-world examples and lessons learned

What is Prompt Engineering?

Definition: Crafting effective instructions to get optimal results from AI language models

Why it matters: Difference between generic responses and getting exactly what you need

Core Principles

Vibe coding is prompting whatever you're doing

LLMs are "pretty stupid" and need clear, well-structured instructions

Evolution of Prompting

Changed from early LLMs to current models

Good prompting reduces cost and complexity in AI systems

"The practice of crafting effective instructions to get optimal results from AI language models"

What Are Context Windows?

Definition: Number of tokens processed simultaneously through the prompt

Token: Represents a word or part of a word

How it works: Every token analyzed against every other token

Evolution of Context Windows

Early GPT-3

~3,000 tokens

Mid-2023

~32,000 tokens

Current Models

Millions of tokens

Managing Context Effectively

LLMs are stateless - no memory between requests

Distractor problem: Irrelevant information reduces effectiveness

Larger context windows aren't always better

Key Considerations

Balance between conversation history and context limits

Know when to start fresh vs continuing a conversation

Strategic information pruning for optimal results

Stateless Nature of LLMs

LLMs have no memory between requests

Each prompt is processed independently

Conversation history must be explicitly included in each prompt

The Distractor Problem

Irrelevant information in context windows reduces effectiveness

Models can get confused by too much context

Larger context windows aren't always better

Strategies for Managing Context

Prune irrelevant conversation history

Summarize long exchanges

Know when to start fresh vs continuing

Prioritize recent and relevant information

Approach	Best For	Token Efficiency
Full History	Short conversations
Summarized History	Medium conversations
Context Pruning	Long conversations

The Minimum Viable Prompt

Two essential components for effective prompts

Task

What you want the model to do

Context

The situation in which you want it performed

Example

Task:

"Give me a recipe"

Context:

"for a chocolate cake with vanilla frosting"

Adding More Context

More context narrows responses

Specific details lead to better outputs

Balance between specificity and flexibility

Before & After

Basic:

"Write an email"

With Context:

"Write a professional email to my team announcing the project deadline extension by two weeks"

Examples

Providing samples of desired outputs to guide the model

"Here's how I want you to format responses: [example]"

Persona

Defining the AI's role and expertise

"You are a master chef from Northern Switzerland"

Format

Specifying output structure for consistency

"Respond in bullet points, JSON format, under 200 words"

Tone

Setting the communication style for appropriate responses

"Use a friendly, conversational tone with occasional humor"

How Components Work Together

Components reinforce each other to create highly specific outputs

Proper combination reduces ambiguity and improves consistency

Strategic use of components minimizes iterations needed

The Four S's Framework

Specificity

Being precise without repetition

Simplicity

Keeping prompts

Mastering Prompt Engineering for AI Development

A Comprehensive Guide to Crafting Effective AI Instructions

Course Overview

lightbulb Module 1: Introduction

visibility Module 2: Context Windows

construction Module 3: Effective Prompts

architecture Module 4: Methodologies

style Module 5: Prompting Styles

integration_instructions Module 6: Applications

compare Module 7: Model Considerations

cases Module 8: Case Studies

Module 1: Introduction to Prompt Engineering

lightbulb What is Prompt Engineering?

psychology Core Principles

history Evolution of Prompting

Module 2: Understanding Context Windows

visibility What Are Context Windows?

history Evolution of Context Windows

settings Managing Context Effectively