LLM Configuration Essentials

Understanding how to configure AI models is crucial for getting the best results. In this lesson, you'll learn about the key parameters that control how AI models behave and how to optimize them for different tasks.

Understanding Model Parameters

AI models have several configurable parameters that affect their behavior. The most important ones are:

Temperature

Temperature controls the randomness of the model's responses. Think of it as the "creativity" setting.

• Low Temperature (0.0 - 0.3): More deterministic, consistent responses
• Medium Temperature (0.3 - 0.7): Balanced creativity and consistency
• High Temperature (0.7 - 1.0): More creative, varied responses

When to Use Different Temperature Settings

Low Temperature (0.0 - 0.3)

• Factual questions and answers
• Code generation
• Data analysis
• Technical writing
• When you need consistent, reliable outputs

Medium Temperature (0.3 - 0.7)

• General conversation
• Content creation
• Problem solving
• Most everyday tasks

High Temperature (0.7 - 1.0)

• Creative writing
• Brainstorming
• Story generation
• When you want diverse, unexpected ideas

Top-p (Nucleus Sampling)

Top-p controls the diversity of word choices by limiting the model to consider only the most likely tokens.

• Low Top-p (0.1 - 0.3): More focused, predictable responses
• Medium Top-p (0.3 - 0.7): Balanced diversity
• High Top-p (0.7 - 1.0): More diverse, creative responses

Max Tokens

Max tokens sets the maximum length of the response. This is important for:

• Controlling response length
• Managing API costs
• Ensuring responses fit your needs

Frequency Penalty

Frequency penalty reduces the likelihood of the model repeating the same words or phrases.

• Low (0.0 - 0.5): Allows some repetition
• High (0.5 - 1.0): Encourages more varied vocabulary

Presence Penalty

Presence penalty reduces the likelihood of the model repeating topics or themes.

• Low (0.0 - 0.5): Allows topic repetition
• High (0.5 - 1.0): Encourages diverse topics

Model-Specific Configurations

GPT Models (OpenAI)

GPT-5 Configuration:

{
  "model": "gpt-5",
  "temperature": 0.7,
  "max_tokens": 1000,
  "top_p": 1.0,
  "frequency_penalty": 0.0,
  "presence_penalty": 0.0
}

GPT-4o Configuration:

{
  "model": "gpt-4o",
  "temperature": 0.7,
  "max_tokens": 1000,
  "top_p": 1.0,
  "frequency_penalty": 0.0,
  "presence_penalty": 0.0
}

Claude Models (Anthropic)

Claude Sonnet 4 Configuration:

{
  "model": "claude-3-5-sonnet-20241022",
  "temperature": 0.7,
  "max_tokens": 1000,
  "top_p": 1.0
}

Claude Opus 4.1 Configuration:

{
  "model": "claude-3-5-opus-20241022",
  "temperature": 0.7,
  "max_tokens": 1000,
  "top_p": 1.0
}

Gemini Models (Google)

Gemini 2.5 Flash Configuration:

{
  "model": "gemini-2.5-flash",
  "temperature": 0.7,
  "max_output_tokens": 1000,
  "top_p": 1.0,
  "top_k": 40
}

Gemini 2.5 Pro Configuration:

{
  "model": "gemini-2.5-pro",
  "temperature": 0.7,
  "max_output_tokens": 1000,
  "top_p": 1.0,
  "top_k": 40
}

xAI Models

Grok 4 Configuration:

Note: Grok 4 is currently only available through X Premium+ subscription and does not have a public API. Configuration options are limited.

{
  "model": "grok-4",
  "temperature": 0.7,
  "max_tokens": 1000
}

Availability: X Premium+ subscribers only API Access: Not publicly available Configuration: Limited parameter control

Recommended Configurations by Task Type

1. Factual/Technical Tasks

{
  "temperature": 0.1,
  "top_p": 0.9,
  "frequency_penalty": 0.0,
  "presence_penalty": 0.0
}

Use for:

• Code generation
• Data analysis
• Technical documentation
• Fact-checking
• Mathematical calculations

2. Creative Writing

{
  "temperature": 0.8,
  "top_p": 0.9,
  "frequency_penalty": 0.3,
  "presence_penalty": 0.3
}

Use for:

• Story writing
• Poetry
• Creative content
• Brainstorming
• Marketing copy

3. Conversational AI

{
  "temperature": 0.7,
  "top_p": 0.9,
  "frequency_penalty": 0.1,
  "presence_penalty": 0.1
}

Use for:

• Chatbots
• Customer service
• General conversation
• Q&A systems

4. Analysis and Summarization

{
  "temperature": 0.3,
  "top_p": 0.9,
  "frequency_penalty": 0.0,
  "presence_penalty": 0.0
}

Use for:

• Text summarization
• Content analysis
• Report generation
• Data interpretation

Context Window Management

Understanding Context Windows

Each model has a maximum context window (the amount of text it can process):

• GPT-5: ~1M+ tokens
• GPT-4o: ~128K tokens
• Claude Sonnet 4: ~200K tokens
• Claude Opus 4.1: ~200K tokens
• Gemini 2.5 Flash: ~1M+ tokens
• Gemini 2.5 Pro: ~1M+ tokens
• Grok 4: ~128K tokens

Best Practices for Context Management

1. Be Concise: Keep prompts focused and relevant
2. Prioritize Information: Put the most important information first
3. Use Summaries: For long documents, provide summaries rather than full text
4. Chunk Large Content: Break large inputs into smaller, manageable pieces

Cost Optimization

Token Usage Strategies

1. Set Appropriate Max Tokens: Don't set unnecessarily high limits
2. Use Shorter Prompts: Be concise and specific
3. Batch Similar Requests: Combine related queries when possible
4. Cache Common Responses: Store frequently requested information

Cost Comparison (Approximate - August 2025)

• GPT-5: ~$0.005 per 1K input tokens, ~$0.015 per 1K output tokens
• GPT-4o: ~$0.0025 per 1K input tokens, ~$0.01 per 1K output tokens
• Claude Sonnet 4: ~$0.003 per 1K input tokens, ~$0.015 per 1K output tokens
• Claude Opus 4.1: ~$0.015 per 1K input tokens, ~$0.075 per 1K output tokens
• Gemini 2.5 Flash: ~$0.0005 per 1K input tokens, ~$0.0015 per 1K output tokens
• Gemini 2.5 Pro: ~$0.0025 per 1K input tokens, ~$0.0075 per 1K output tokens
• Grok 4: X Premium+ subscription only (no public API pricing)

Practical Configuration Examples

Example 1: Code Generation

{
  "temperature": 0.1,
  "max_tokens": 2000,
  "top_p": 0.9,
  "frequency_penalty": 0.0,
  "presence_penalty": 0.0
}

Prompt:

You are an expert Python developer. Write a function to sort a list of dictionaries by a specific key. Use clear variable names and include docstring documentation.

Requirements:
- Function name: sort_dict_list
- Parameters: list of dictionaries, sort key
- Return: sorted list
- Include error handling

Example 2: Creative Story Writing

{
  "temperature": 0.8,
  "max_tokens": 1500,
  "top_p": 0.9,
  "frequency_penalty": 0.3,
  "presence_penalty": 0.2
}

Prompt:

You are a creative storyteller. Write a short story (300-500 words) about a character who discovers a mysterious door in their house. The story should be engaging and include elements of mystery and wonder.

Style: Modern fantasy
Tone: Whimsical but slightly mysterious

Example 3: Data Analysis

{
  "temperature": 0.2,
  "max_tokens": 1000,
  "top_p": 0.9,
  "frequency_penalty": 0.0,
  "presence_penalty": 0.0
}

Prompt:

You are a data analyst. Analyze the following dataset and provide insights:

[Dataset description and sample data]

Please provide:
1. Key trends and patterns
2. Statistical summary
3. Potential insights
4. Recommendations for further analysis

Testing and Iteration

A/B Testing Configurations

1. Test Different Temperatures: Try 0.1, 0.5, and 0.9 for the same prompt
2. Compare Model Performance: Test the same task across different models
3. Measure Consistency: Run the same prompt multiple times to check consistency
4. Evaluate Quality: Assess output quality against your requirements

Configuration Checklist

• [ ] Temperature set appropriately for task type
• [ ] Max tokens optimized for expected response length
• [ ] Top-p configured for desired creativity level
• [ ] Penalties set to avoid unwanted repetition
• [ ] Context window usage optimized
• [ ] Cost considerations factored in

Common Configuration Mistakes

1. Too High Temperature for Technical Tasks: Leads to inconsistent, unreliable outputs
2. Too Low Temperature for Creative Tasks: Results in boring, repetitive content
3. Unnecessarily High Max Tokens: Wastes tokens and increases costs
4. Ignoring Context Window Limits: Can cause errors or incomplete responses
5. Not Testing Different Configurations: Missing opportunities for optimization

Next Steps

In the next lesson, you'll learn about The CLEAR Framework and how to structure your prompts for maximum effectiveness.

Practice Exercise: Try configuring the same prompt with different temperature settings (0.1, 0.5, 0.9) and observe how the responses change. This will help you understand the impact of configuration parameters.