Overview
TeamAI provides multiple AI models optimized for different use cases. This guide explains model characteristics, selection criteria, and testing methodologies to ensure optimal model-task alignment.
Prerequisites
Active TeamAI account with platform access
Defined task or query objective
Test prompt for model evaluation (recommended)
Available Models
TeamAI's model nomenclature reflects primary optimization focus, simplifying selection based on task characteristics.
Model Overview
Model | Primary Optimization | Typical Use Cases |
Fast | Response speed | Simple queries, quick lookups, time-sensitive tasks |
Smart | Response quality | General-purpose tasks requiring depth and accuracy |
Code | Technical tasks | Software development, data analysis, technical documentation |
Reasoning | Complex analysis | Multi-step problems, strategic analysis, detailed explanations |
Selection Criteria
Consider the following factors when selecting a model:
Task complexity – Simple vs. multi-faceted problems
Required depth – Quick answer vs. comprehensive analysis
Technical specificity – General knowledge vs. specialized coding/data tasks
Time constraints – Immediate response vs. thorough investigation
Model Selection Interface
Tooltip Guidance
The platform provides contextual guidance for model selection.
Access Method:
Hover over or click the information icon adjacent to each model name
Review recommended use cases and model strengths
Match recommendations to current task requirements
Purpose: Tooltips provide quick-reference guidance without requiring empirical testing, particularly beneficial for new users or unfamiliar model types.
Comparative Testing Methodology
Process Overview
Direct comparison provides empirical evidence for model selection decisions.
Steps:
Formulate a representative test prompt
Submit prompt using initial model selection
Click "Regenerate with new model" button (appears above response)
Select alternative model from dropdown menu
Compare responses across dimensions:
Response quality
Depth of analysis
Relevance to query
Processing time
Benefit: Comparative testing reveals model-specific interpretation and response patterns for your specific query type.
Model Response Characteristics
Model | Key Characteristics | Suitable For |
Fast | • Concise, abbreviated responses • Minimal elaboration • Optimized processing time | • Factual lookups • Simple definitions • Quick confirmations |
Smart | • Enhanced comprehension • Detailed information delivery • Balanced speed-quality tradeoff | • General research • Content creation • Explanation requests |
Code | • Technical accuracy emphasis • Code snippet generation • Data structure analysis | • Software development • Debugging • Algorithm design • Data processing |
Reasoning | • Visible analytical process • Multi-stage problem decomposition • Comprehensive response depth | • Strategic planning • Complex problem-solving • Research synthesis • Detailed analysis |
Best Practices
Match model to task complexity – Avoid over-engineering simple queries
Prioritize speed vs. depth based on time constraints and accuracy requirements
Use Code Model for technical work – Leverage domain-specific optimization
Test with representative prompts – Evaluate models using queries similar to actual use cases
Leverage regeneration feature – Compare models within existing conversations rather than creating new chats
Iterate based on results – Adjust model selection as task requirements evolve
Summary
Effective model selection requires understanding the relationship between task characteristics and model capabilities. Utilize tooltips for quick guidance, employ comparative testing for critical decisions, and adjust selections based on empirical results. The platform's flexibility enables dynamic optimization as requirements evolve.
Key Takeaway: The optimal model is task-dependent, not universally superior. Matching model capabilities to specific requirements maximizes efficiency and output quality.
FAQ
Q: Should the most advanced model be used for all tasks?
A: No. Model selection should align with task complexity. Advanced models provide unnecessary depth for simple queries and consume additional processing time. Select the minimum-capability model that meets requirements.
Q: How significant are performance differences between models?
A: Differences can be substantial. The Reasoning Model may provide 3-5x more detailed analysis compared to the Fast Model, including explicit problem-solving steps.
Q: Can models be switched during an active conversation?
A: Yes. The "regenerate with new model" feature allows in-conversation model switching, enabling direct comparison without context reset.
Q: Which model is optimal for programming tasks?
A: The Code Model is specifically optimized for software development, debugging, and data analysis tasks.
Q: Do model choices impact response time or resource consumption?
A: Yes. Fast Model prioritizes speed over depth, while Reasoning Model prioritizes comprehensive analysis over response time. Consider this tradeoff when selecting models.
Q: What model is recommended for new users?
A: The Smart Model provides balanced performance suitable for general exploration. As familiarity increases, transition to specialized models for specific use cases.
Q: How do I know if I've chosen the wrong model?
A: Indicators include: insufficient detail, excessive verbosity for simple needs, lack of technical accuracy in code responses, or inadequate reasoning depth for complex problems. Use the regeneration feature to test alternatives.