Working with Models

AIA supports multiple AI models through the RubyLLM gem, allowing you to choose the best model for each task and even use multiple models simultaneously.

Available Models

List All Models

# Show all available models
aia --available_models

# Filter by provider
aia --available_models openai
aia --available_models anthropic
aia --available_models google

# Filter by capability
aia --available_models vision
aia --available_models function_calling
aia --available_models text_to_image

# Complex filtering
aia --available_models openai,gpt,4

Model Information

Each model listing includes: - Model ID: Exact name to use with --model - Provider: Company providing the model - Context Window: Maximum input/output length - Input Cost: Price per million input tokens - Modalities: Supported input/output types - Capabilities: Special features available

Model Selection

Single Model Usage

# Use specific model
aia --model gpt-4 my_prompt
aia --model claude-3-sonnet code_review.py
aia --model gemini-pro analyze_data.csv

# Short model names (when unambiguous)
aia --model gpt-4 my_prompt
aia --model claude my_prompt
aia --model gemini my_prompt

Model Categories by Use Case

Text Generation

Creative Writing: High creativity, good with narratives - gpt-4: Excellent creative writing, good instruction following - claude-3-sonnet: Great for longer creative pieces - gemini-pro: Good balance of creativity and structure

Technical Writing: Accuracy and precision focus - gpt-4: Strong technical accuracy - claude-3-sonnet: Excellent for documentation - gpt-3.5-turbo: Fast, cost-effective for simple technical tasks

Code Analysis

Code Review: Understanding existing code - gpt-4: Excellent code comprehension - claude-3-sonnet: Great at explaining complex code - codellama-34b: Specialized for code understanding

Code Generation: Writing new code - gpt-4: High-quality code generation - claude-3-sonnet: Good at following coding standards - codellama-7b: Fast code completion

Data Analysis

Statistical Analysis: Working with numbers and data - claude-3-sonnet: Excellent analytical reasoning - gpt-4: Strong mathematical capabilities - gemini-pro: Good with structured data

Research: Processing large amounts of information - claude-3-sonnet: Large context window, good summarization - gpt-4: Strong reasoning and synthesis - claude-3-opus: Highest quality analysis (more expensive)

Multi-Model Operations

Parallel Processing

Run the same prompt with multiple models:

# Compare outputs from different models
aia --model "gpt-4,claude-3-sonnet,gemini-pro" my_prompt

# Each model provides separate response

Consensus Mode

Get unified response from multiple models:

# Enable consensus mode
aia --model "gpt-4,claude-3-sonnet" --consensus my_prompt

# Works in chat mode too
aia --chat --model "gpt-4o-mini,gpt-3.5-turbo" --consensus

# Models collaborate to provide single, refined response

Consensus Output Format:

from: gpt-4o-mini (consensus)
Based on the insights from multiple AI models, here is a comprehensive answer that
incorporates the best perspectives and resolves any contradictions...

Individual Response Mode

By default, each model provides its own separate response:

# Default behavior - show individual responses  
aia --model "gpt-4o-mini,gpt-3.5-turbo,gpt-5-mini" my_prompt

# Explicitly disable consensus
aia --model "gpt-4o-mini,gpt-3.5-turbo" --no-consensus my_prompt

Individual Responses Output Format:

from: gpt-4o-mini
Response from the first model...

from: gpt-3.5-turbo  
Response from the second model...

from: gpt-5-mini
Response from the third model...

Model Configuration Status

View your current multi-model configuration using the //model directive:

# In any prompt file or in chat session
//model

Example Output:

Multi-Model Configuration:
==========================
Model count: 3
Primary model: gpt-4o-mini (used for consensus when --consensus flag is enabled)
Consensus mode: false

Model Details:
--------------------------------------------------
1. gpt-4o-mini (primary)
2. gpt-3.5-turbo  
3. gpt-5-mini

Multi-Model Features: - Primary Model: The first model in the list serves as the consensus orchestrator - Concurrent Processing: All models run simultaneously for better performance
- Flexible Output: Choose between individual responses or synthesized consensus - Error Handling: Invalid models are reported but don't prevent valid models from working - Batch Mode Support: Multi-model responses are properly formatted in output files

Model Comparison in Prompts

Compare responses from multiple models:
//compare "Explain quantum computing" --models gpt-4,claude-3-sonnet,gemini-pro

Which explanation is most accessible?

Model Configuration

Model-Specific Settings

Different models may work best with different parameters:

GPT Models

# ~/.aia/models/gpt-4.yml
temperature: 0.7
max_tokens: 4000
top_p: 1.0
frequency_penalty: 0.0
presence_penalty: 0.0

Claude Models

# ~/.aia/models/claude-3-sonnet.yml
temperature: 0.8
max_tokens: 8000
top_p: 0.9

Gemini Models

# ~/.aia/models/gemini-pro.yml
temperature: 0.6
max_tokens: 2000
top_p: 0.95

Dynamic Model Selection

Choose models based on task characteristics:

# In prompt with Ruby directive
//ruby
task_type = '<%= task_type %>'
model = case task_type
        when 'creative' then 'gpt-4'
        when 'analytical' then 'claude-3-sonnet'  
        when 'code' then 'codellama-34b'
        else 'gpt-3.5-turbo'
        end
puts "//config model #{model}"

Model Performance Optimization

Speed vs Quality Tradeoffs

Fast Models (Lower Cost, Quicker Response)

# Quick tasks, simple questions
aia --model gpt-3.5-turbo simple_question

# Code completion, basic analysis
aia --model claude-3-haiku code_completion

# Bulk processing
for file in *.txt; do
  aia --model gpt-3.5-turbo --out_file "${file%.txt}_processed.md" process_file "$file"
done

Quality Models (Higher Cost, Better Results)

# Complex analysis, important decisions
aia --model gpt-4 strategic_analysis.md

# Creative writing, nuanced tasks
aia --model claude-3-opus --temperature 1.0 creative_writing

# Critical code review
aia --model gpt-4 --temperature 0.2 security_review.py

Context Window Management

Large Context Models

For processing large documents:

# Claude has the largest context window
aia --model claude-3-sonnet large_document.pdf

# GPT-4 Turbo for large contexts
aia --model gpt-4-turbo comprehensive_analysis.md

Context-Aware Processing

# Check document size and choose appropriate model
//ruby
file_size = File.size('<%= file %>') 
model = file_size > 100000 ? 'claude-3-sonnet' : 'gpt-4'
puts "//config model #{model}"

# Process with selected model

Model Capabilities

Vision Models

For image analysis and processing:

# Analyze images
aia --model gpt-4-vision image_analysis.jpg

# Process screenshots
aia --model gpt-4-vision --temperature 0.3 screenshot_analysis.png

# Extract text from images
aia --model gpt-4-vision extract_text_prompt image_with_text.jpg

Function Calling Models

For tool integration:

# Models that support function calling
aia --model gpt-4 --tools ./tools/ analysis_with_tools

# Best function calling models
aia --model gpt-3.5-turbo --tools ./tools/ tool_heavy_task

Code Models

Specialized for programming tasks:

# Code-specific models
aia --model codellama-34b code_generation_task

# Programming assistance
aia --model codellama-7b --temperature 0.1 debug_assistance

Cost Management

Model Pricing Considerations

Monitor Usage

# Enable verbose mode to see token usage
aia --verbose --model gpt-4 expensive_task

# Use debug mode for detailed cost tracking
aia --debug --model claude-3-opus cost_analysis

Cost-Effective Strategies

# Use cheaper models for initial drafts
aia --model gpt-3.5-turbo initial_draft

# Refine with better models
aia --model gpt-4 --include initial_draft.md refine_output

# Batch processing with efficient models
aia --model claude-3-haiku --pipeline "process,summarize" batch_files/

Budget-Conscious Model Selection

# Cost-effective configuration
budget_models:
  fast_tasks: gpt-3.5-turbo
  analysis: claude-3-haiku
  creative: gpt-3.5-turbo

premium_models:
  critical_analysis: gpt-4
  creative_writing: claude-3-sonnet
  complex_reasoning: claude-3-opus

Model-Specific Tips

GPT Models

• GPT-4: Best for complex reasoning, creative tasks
• GPT-3.5 Turbo: Fast, cost-effective, good general model
• GPT-4 Vision: Excellent for image analysis
• Best for: Code generation, creative writing, general tasks

Claude Models

• Claude-3 Opus: Highest quality, most expensive
• Claude-3 Sonnet: Great balance of quality and cost
• Claude-3 Haiku: Fastest, most cost-effective
• Best for: Long documents, analytical tasks, following instructions

Gemini Models

• Gemini Pro: Google's flagship model
• Gemini Pro Vision: Multimodal capabilities
• Best for: Structured data, mathematical reasoning

Specialized Models

• CodeLlama: Open-source code generation
• Llama 2: Open-source general purpose
• Mixtral: High-performance open model

Troubleshooting Models

Common Issues

Model Not Available

# Check if model exists
aia --available_models | grep model_name

# Try alternative model names
aia --available_models anthropic

Authentication Errors

# Check API keys
echo $OPENAI_API_KEY
echo $ANTHROPIC_API_KEY

# Test with working model
aia --model gpt-3.5-turbo test_prompt

Context Length Exceeded

# Use model with larger context
aia --model claude-3-sonnet large_document.pdf

# Split large inputs
split -l 1000 large_file.txt chunk_
for chunk in chunk_*; do
  aia --model gpt-4 process_chunk "$chunk"
done

Rate Limiting

# Add delays between requests
sleep 1 && aia --model gpt-4 request1
sleep 1 && aia --model gpt-4 request2

# Use different model to avoid limits
aia --model claude-3-sonnet alternative_processing

Advanced Model Usage

Model Switching Workflows

# Start with fast model for initial processing
aia --model gpt-3.5-turbo --out_file draft.md initial_analysis data.csv

# Switch to quality model for refinement
aia --model gpt-4 --include draft.md --out_file final.md refine_analysis

# Use specialized model for specific tasks
aia --model gpt-4-vision --include final.md image_analysis charts/

Conditional Model Selection

# Dynamic model selection based on task complexity
//ruby
content_length = File.read('<%= input_file %>').length
complexity = content_length > 10000 ? 'high' : 'low'

model = case complexity
        when 'high' then 'claude-3-sonnet'
        when 'low' then 'gpt-3.5-turbo'
        end

puts "//config model #{model}"
puts "Selected #{model} for #{complexity} complexity task"

Model Ensemble Techniques

# Use different models for different aspects
aia --model gpt-4 --out_file technical_analysis.md technical_review code.py
aia --model claude-3-sonnet --out_file style_analysis.md style_review code.py
aia --model gpt-3.5-turbo --include technical_analysis.md --include style_analysis.md synthesize_reviews

Integration with Other Features

Chat Mode Model Management

# Start chat with specific model
aia --chat --model gpt-4

# Switch models during chat
You: /model claude-3-sonnet
AI: Switched to claude-3-sonnet

# Compare models in chat
You: //compare "Explain this concept" --models gpt-4,claude-3-sonnet

Pipeline Model Configuration

# Different models for different pipeline stages
aia --config_file pipeline_config.yml --pipeline "extract,analyze,report"

# pipeline_config.yml
extract:
  model: gpt-3.5-turbo
analyze:
  model: claude-3-sonnet
report:
  model: gpt-4

Tool Integration

# Use models optimized for function calling with tools
aia --model gpt-3.5-turbo --tools ./analysis_tools/ data_processing

# Vision models with image processing tools
aia --model gpt-4-vision --tools ./image_tools/ visual_analysis

Related Documentation

• Available Models - Complete model list
• Configuration - Model configuration options
• CLI Reference - Command-line model options
• Chat Mode - Interactive model usage
• Advanced Prompting - Model-specific prompting techniques

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Choosing the right model for each task is crucial for optimal results. Experiment with different models to find what works best for your specific use cases!