Model Selection

Introduction

Duvo allows you to choose which AI model powers your assignments. Different models have different strengths, and selecting the right one can improve your assignment's performance for specific tasks.

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Why Model Selection Matters

Different AI models excel at different types of tasks:

• Complex reasoning - Some models are better at multi-step logic and analysis

• Speed - Faster models are ideal for simple, high-volume tasks

• Specialized tasks - Certain models perform better with code, data, or creative content

Choosing the right model helps you get better results and optimize performance.

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How to Select a Model

When Building an Assignment

1. Open the assignment you're building or editing

2. Find the Model selection option in the configuration

3. Choose from the available models

4. Save your changes

Changing Models Later

You can change an assignment's model at any time:

1. Open the assignment

2. Edit the configuration

3. Select a different model

4. Save and test the changes

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Choosing the Right Model

For Complex Analysis

When your assignment needs to:

• Analyze detailed documents

• Make nuanced decisions

• Handle multi-step reasoning

• Process complex instructions

Choose a more capable model that excels at reasoning.

For Simple Tasks

When your assignment needs to:

• Perform straightforward data entry

• Execute simple, repetitive actions

• Handle high-volume processing

• Complete quick lookups

A faster model may be more efficient.

For Specialized Work

Consider the nature of your task:

• Document work - Models with strong comprehension

• Data processing - Models good with structured information

• Communication - Models that write naturally

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Best Practices

Start with Defaults

The default model works well for most tasks. Only change if you have a specific reason.

Test Before Committing

When changing models:

1. Run test executions with the new model

2. Compare results to previous runs

3. Verify the assignment still works correctly

4. Monitor for any issues after switching

Match Model to Task

Don't assume more capable always means better:

• Simpler models can be faster and more efficient

• Complex models may be overkill for basic tasks

• The right fit depends on your specific needs

Document Your Choice

If you select a specific model for a reason, note why in the assignment description. This helps future maintenance.

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Things to Know

• Model availability may vary based on your plan

• Changing models doesn't affect past job history

• Some tasks may perform differently with different models

• Model capabilities are continuously improved

• Default selections are optimized for general use