Module 1: From Prompts to Context Systems

Overview

Welcome to the paradigm shift that's transforming AI from a fancy autocomplete into a genuine thinking partner. In this module, you'll discover why even the best prompt engineering hits a wall and how context engineering breaks through that barrier.

What You'll Learn

Why prompt engineering alone isn't enough for complex tasks
How context windows function as your AI's working memory
The fundamental principles that make context engineering powerful
Real examples showing the dramatic difference context makes

Prerequisites

Basic experience using Claude or ChatGPT
Familiarity with writing prompts
30 minutes of focused learning time

The Prompt Engineering Wall

Where We've Been

For the past two years, we've been obsessed with crafting the perfect prompt. We've learned techniques like:

Zero-shot prompting: Direct instructions
Few-shot prompting: Providing examples
Chain of thought: Step-by-step reasoning
Role prompting: "You are an expert..."

And they work! But here's the dirty secret nobody talks about: they all hit the same fundamental limitation.

The Context Window Crisis

Picture this scenario (I bet you've been here):

You: "Help me refactor this code to follow our team's patterns" AI: "Sure! What patterns does your team follow?" You: Explains patterns AI: Gives decent suggestion You: "Now do the same for this other file" AI: "What patterns should I follow?" You: 😤

This is the prompt engineering wall. No matter how perfectly you craft your prompt, the AI forgets everything the moment you start a new conversation or hit the context limit.

Understanding Context Windows

Context Windows as AI RAM

Think of context windows like your computer's RAM:

Computer RAM	AI Context Window
Stores active programs	Stores active conversation
Limited size (8GB, 16GB, etc.)	Limited tokens (100K, 200K, etc.)
Cleared on restart	Cleared on new chat
More RAM = more capability	Larger context = more capability

Token Mathematics

1 token ≈ 4 characters (rough estimate)

100K context = ~75,000 words
Average conversation = 2-5K tokens
Complex codebase explanation = 20-50K tokens
Full project context = Often exceeds limits

The Real Problem

It's not just about size. It's about what fills that space:

Traditional Approach:

[System Prompt: 500 tokens]
[Your Question: 200 tokens]
[AI Response: 1000 tokens]
[Your Follow-up: 200 tokens]
[Previous Context: Gets dropped or compressed]
Total: Quickly fills up, loses important context

Context Engineering Approach:

[System Architecture: Dynamically loaded]
[Relevant Code: Selectively included]
[Conversation History: Intelligently compressed]
[External Memory: Infinite storage via databases]
Total: Maintains critical context indefinitely

Context Engineering Principles

1. Dynamic Information Architecture

Instead of static prompts, build systems that adapt:

Static (Prompt Engineering):

You are a Python expert. Follow PEP 8. Use type hints.
Always write tests. Prefer functional programming.

Dynamic (Context Engineering):

# System analyzes your codebase and discovers:
- Project uses Django with DRF
- Team prefers class-based views
- Existing test patterns use pytest
- Code style matches Black formatter

# Dynamically includes only relevant context

2. Stateful Intelligence

Transform one-shot interactions into continuous learning:

Stateless (Traditional):

Every conversation starts from zero
No memory of previous discussions
Constantly re-explaining context

Stateful (Context Engineered):

Remembers all previous interactions
Builds on past decisions
Improves with each conversation

3. Tool Orchestration

Move beyond text to active capabilities:

Before: AI suggests what you should do After: AI actually does it (with your permission)

Real-World Examples

Example 1: Documentation Assistant

Prompt Engineering Approach:

Write documentation for my API endpoint that handles user authentication.
Include examples and error cases.

Result: Generic documentation that misses your specific implementation details.

Context Engineering Approach:

# System automatically includes:
- Your actual authentication code
- Existing documentation patterns
- Team's documentation standards
- Related endpoints for consistency
- Previous documentation feedback

# Result: Documentation that perfectly matches your system

Example 2: Debugging Complex Issues

Prompt Engineering Approach:

I'm getting a null pointer exception in my payment processing. 
Here's the error: [paste error]

Context Engineering Approach:

# System automatically:
1. Loads relevant code files
2. Traces execution path
3. Includes related test cases
4. Checks recent commits
5. Compares with working versions
6. Suggests fix with full context

# Finds issues like:
"This started after commit abc123 changed the order 
of operations in the payment validator"

Example 3: Multi-Session Projects

Building a feature over multiple days:

Day 1: Design phase Day 2: Implementation Day 3: Testing Day 4: Refinement

With prompt engineering: Start from scratch each day With context engineering: Pick up exactly where you left off

The SCIC Framework Introduction

Context engineering isn't random - it follows a systematic approach:

Select

Choose what information matters:

Not everything is relevant
Quality over quantity
Dynamic selection based on task

Compress

Fit more into limited space:

Summarize previous conversations
Extract key decisions
Maintain critical details

Isolate

Prevent context contamination:

Separate concerns
Domain-specific agents
Clean mental models

Compose

Orchestrate multiple contexts:

Combine different knowledge sources
Coordinate agent actions
Maintain coherent state

Practical Exercise

Before You Continue

Try this experiment to feel the difference:

Part 1: Traditional Approach

Open a new Claude chat
Ask: "Help me design a REST API for a task management system"
Get the response
Open another new chat
Ask: "Now implement the user authentication for that API"
Notice how you need to re-explain everything

Part 2: Context Thinking

Open a new Claude chat
Say: "I'm designing a REST API for a task management system. Before we start, let me give you the full context..."
Provide:
- Tech stack details
- Team conventions
- Security requirements
- Similar existing systems
Then ask for the design
For implementation, reference the previous context

Observe: How much better is the second response?

Key Concepts Summary

Context Engineering Is:

✅ Building systems that manage information flow
✅ Creating persistent memory across sessions
✅ Orchestrating multiple AI agents
✅ Dynamically adapting to your needs

Context Engineering Is NOT:

❌ Just writing longer prompts
❌ Dumping everything into context
❌ A specific tool or framework
❌ Only for developers

Checkpoint Task

Your Mission

Compare the same complex task using basic prompting vs context thinking:

Choose a complex task from your work:
- Debugging a specific issue
- Designing a new feature
- Writing comprehensive documentation
- Analyzing data patterns
Attempt with basic prompting:
- Time how long it takes
- Note where you need to re-explain
- Save the final output
Attempt with context thinking:
- First, list all relevant context
- Organize information by importance
- Provide context systematically
- Time the process
- Save the output
Document the differences:
- Quality of output
- Time invested
- Frustration level
- Completeness of solution

Success Criteria

✅ Identified at least 5 pieces of context that matter
✅ Saw measurable improvement in output quality
✅ Understood why context beats prompts
✅ Ready to learn the SCIC framework

Next Steps

You've seen why context engineering matters. In Module 2, you'll master the SCIC framework that makes it systematic and repeatable.

Preview of Module 2

Deep dive into SELECT strategies
Compression algorithms that preserve meaning
Isolation patterns that prevent confusion
Composition techniques for complex systems

Ready to continue? Module 2 awaits with hands-on SCIC implementation!

From Prompts to Context Systems

Prerequisites

Next Steps

Your Progress