Chapter 3: Practical Exercises - Model Mastery

30 Minutes to Multi-Model Expertise

These exercises will help you understand each AI model's unique strengths and build muscle memory for choosing the right tool for each task.

Exercise 1: The Model Shootout (5 minutes)

Objective

Compare how different models handle the same creative task.

Setup

Create creative-test.md:

# Creative Writing Test

## Prompt
Write a 50-word description of a rainy day that evokes emotion without using the words: rain, wet, water, or drops.

Steps

1. Select the prompt
2. Test each model:
   • GPT-4o
   • Claude 4 Sonnet
   • Gemini 2.0 Flash
   • Llama 3.3
3. Document responses below each other
4. Compare for:
   • Creativity
   • Emotional impact
   • Following constraints
   • Writing style

Expected Insights

• Claude often excels at creative writing
• GPT-4o may be more technically precise
• Gemini might offer unique perspectives
• Llama provides solid free alternative

Exercise 2: Context Window Challenge (7 minutes)

Objective

Test how different models handle long documents.

Setup

1. Create long-doc-test.md
2. Paste a long document (3-5 pages)
3. Add prompt at the end:

   Summarise the key points from this document, 
   including specific details from the beginning, 
   middle, and end.
   

Test Sequence

1. GPT-4o: Note any truncation warnings
2. Claude 4 Sonnet: Should handle full document (200k context)
3. Gemini 2.0 Pro: Best for very long content (2M context)
4. Compare comprehensiveness of summaries

Learning Points

• When you need GPT-4o vs Gemini for long docs
• How context limits affect output quality
• Cost implications of long context

Exercise 3: Speed vs Quality Test (5 minutes)

Objective

Find the sweet spot between speed, quality, and cost.

Task Types

Create speed-test.md with three tasks:

# Speed vs Quality Comparison

## Task 1: Simple Email
Draft a professional email declining a meeting invitation.

## Task 2: Complex Analysis
Explain the pros and cons of remote work for businesses.

## Task 3: Code Generation
Write a Python function to calculate compound interest.

Testing Matrix

Task	GPT-4o-mini	GPT-4o	Claude 4 Sonnet	Your Rating
Email	[Test]	[Test]	[Test]	Speed/Quality/Cost
Analysis	[Test]	[Test]	[Test]	Speed/Quality/Cost
Code	[Test]	[Test]	[Test]	Speed/Quality/Cost

Key Findings

Document when to use:

• Fast models (mini versions)
• Premium models (GPT-4o/Claude)
• Balanced options

Exercise 4: Multi-Model Workflow (8 minutes)

Objective

Build a workflow using different models for their strengths.

Scenario: Research Report Creation

1. Research Phase (Gemini 2.0 Pro)

   Prompt: Research and summarise current trends in [your industry]
   

2. Outline Phase (GPT-4o)

   Prompt: Create a detailed outline for a report on these trends
   

3. Writing Phase (Claude 4 Sonnet)

   Prompt: Write the introduction section based on this outline
   

4. Review Phase (GPT-4o-mini)

   Prompt: Proofread and suggest improvements
   

Document Your Workflow

# My Multi-Model Workflow

## Best Models for Each Stage:
- Research: [Your choice] because...
- Structure: [Your choice] because...
- Writing: [Your choice] because...
- Editing: [Your choice] because...

Exercise 5: Cost Optimisation Practice (5 minutes)

Objective

Learn to minimise costs while maintaining quality.

Create Cost Comparison

cost-analysis.md:

# Cost Analysis Exercise

## Task: Generate a 500-word blog post about productivity

### Approach 1: All Premium
- Model: GPT-4o
- Estimated tokens: 1,000 in + 700 out
- Cost: £[calculate]

### Approach 2: Smart Mix
- Outline: GPT-4o-mini (200 tokens)
- First draft: GPT-4o-mini (800 tokens)
- Polish: Claude 4 Sonnet (500 tokens)
- Total cost: £[calculate]

### Approach 3: Free Options
- Model: Llama 3.3 via Groq
- Cost: £0.00
- Quality comparison: [test and note]

Calculate Real Savings

• Premium only: £___
• Smart mix: £___
• Savings: ___%

Challenge Exercise: Your Custom Comparison

Create Your Test Suite

Based on your actual work needs:

1. Identify 3 common tasks you do
2. Create test prompts for each
3. Test across all models
4. Document which model wins for each task
5. Calculate monthly cost savings

Template

# My Personal Model Preferences

## Task 1: [Your Task]
- Best Model: [...]
- Why: [...]
- Monthly savings: £[...]

## Task 2: [Your Task]
- Best Model: [...]
- Why: [...]
- Monthly savings: £[...]

## Task 3: [Your Task]
- Best Model: [...]
- Why: [...]
- Monthly savings: £[...]

## Total Monthly Savings: £[...]

Exercise Reflection

Quick Assessment

After completing exercises, you should know:

• Which model writes most naturally
• Which model handles long documents best
• When to use expensive vs cheap models
• How to combine models effectively
• Your potential monthly savings

Key Insights Template

# My AI Model Insights

## Writing Tasks
Best model: ___ because ___

## Analysis Tasks  
Best model: ___ because ___

## Long Documents
Best model: ___ because ___

## Quick Tasks
Best model: ___ because ___

## My Monthly AI Budget
Old way (subscriptions): £___
New way (APIs): £___
Savings: £___ (___%)

Pro Tips from Exercises

1. Claude 4 Sonnet often wins for natural writing and coding
2. GPT-4o excels at logical analysis and balanced tasks
3. Gemini 2.0 is unbeatable for long documents (2M context)
4. Mini models are perfect for simple tasks
5. Mixing models optimises cost/quality
6. o1 provides best reasoning for complex problems

Common Patterns Discovered

The 80/20 Rule

• 80% of tasks need only cheap models
• 20% benefit from premium models
• Result: 70%+ cost savings

The Context Ladder

Your New Superpower

You can now:

• Choose the perfect model for any task
• Save 70-90% on AI costs
• Get better results than subscription users
• Work with documents of any size
• Optimise for speed or quality as needed

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Next: Chapter 4: Real Project - Multi-Model Magic

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