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LLM Selection Feature - Use Cases & User Guide

Overview​

This document outlines the use cases, user workflows, and practical applications of the LLM selection feature in the Angular chat application. The feature allows users to configure, test, and switch between multiple AI language models seamlessly.

Primary Use Cases​

Use Case 1: Multi-Model AI Development & Testing​

Scenario: A developer wants to compare responses from different AI models for the same prompt.

User Story:

"As a developer, I want to test how different AI models respond to the same question so I can choose the best model for my specific use case."

Workflow:

  1. Configure multiple API keys (OpenAI GPT-4, Google Gemini Flash 2.5, Anthropic Claude)
  2. Test each API key to ensure they're working
  3. Ask the same question while switching between models
  4. Compare response quality, speed, and style
  5. Select the preferred model for ongoing conversations

Benefits:

  • Side-by-side model comparison
  • Real-time performance evaluation
  • Cost optimization by choosing the most efficient model

Use Case 2: Cost-Optimized AI Usage​

Scenario: A business wants to use different AI models based on query complexity to optimize costs.

User Story:

"As a business owner, I want to use cheaper models for simple queries and premium models for complex tasks to minimize AI costs while maintaining quality."

Workflow:

  1. Configure multiple API keys with different cost structures:
    • Google Gemini Flash 2.5 (fast, cost-effective)
    • OpenAI GPT-4 (premium, expensive)
    • Anthropic Claude (balanced)
  2. Use Gemini Flash for quick questions and brainstorming
  3. Switch to GPT-4 for complex analysis and coding tasks
  4. Monitor usage patterns and costs

Benefits:

  • Reduced operational costs
  • Optimized model selection per task type
  • Budget control and monitoring

Use Case 3: Specialized Model Selection​

Scenario: A user needs different AI models for different types of tasks based on their strengths.

User Story:

"As a content creator, I want to use the best AI model for each specific task - coding, writing, analysis, or creative work."

Workflow:

  1. Configure specialized models:
    • Google Gemini Flash 2.5 for general conversation and quick tasks
    • OpenAI GPT-4 for coding and technical documentation
    • Anthropic Claude for creative writing and analysis
  2. Switch models based on task type:
    • Coding session: Select OpenAI GPT-4
    • Creative writing: Switch to Anthropic Claude
    • Quick questions: Use Gemini Flash 2.5
  3. Maintain separate conversation threads for different purposes

Benefits:

  • Optimized results for specific task types
  • Leveraging each model's unique strengths
  • Improved productivity and output quality

Use Case 4: API Reliability & Backup​

Scenario: A user wants backup options when their primary AI service is down or rate-limited.

User Story:

"As a power user, I want multiple AI providers configured so I can continue working even if one service is experiencing issues."

Workflow:

  1. Configure multiple API keys from different providers
  2. Set primary model (e.g., Google Gemini Flash 2.5)
  3. When primary service fails or is rate-limited:
    • Receive error message in chat
    • Quickly switch to backup model (e.g., OpenAI or Anthropic)
    • Continue conversation without losing context
  4. Switch back to primary when service is restored

Benefits:

  • Uninterrupted workflow
  • Service redundancy
  • Reduced dependency on single provider

Use Case 5: Team Collaboration with Shared Models​

Scenario: A team wants to standardize on specific AI models while allowing individual preferences.

User Story:

"As a team lead, I want to ensure my team uses approved AI models while allowing them to choose the best model for their specific tasks."

Workflow:

  1. Team lead provides approved API keys for:
    • Google Gemini Flash 2.5 (approved for general use)
    • OpenAI GPT-4 (approved for technical tasks)
  2. Team members configure the provided keys
  3. Each member can switch between approved models based on their needs
  4. Consistent model access across the team
  5. Centralized cost tracking and management

Benefits:

  • Standardized AI access across team
  • Cost control and monitoring
  • Flexibility for individual task optimization

Technical Use Cases​

Use Case 6: API Key Testing & Validation​

Scenario: A user wants to verify their API keys are working before important tasks.

User Story:

"As a user, I want to test my API keys to ensure they're working properly before starting important conversations."

Workflow:

  1. Open settings modal
  2. Add new API key for Google Gemini Flash 2.5
  3. Click the test button to verify the key works
  4. See green checkmark for successful test
  5. Activate the tested key for use
  6. Start chatting with confidence

Benefits:

  • Prevents failed conversations due to invalid keys
  • Immediate feedback on API key status
  • Confidence in system reliability

Use Case 7: Model Performance Monitoring​

Scenario: A user wants to track which models perform best for their specific use cases.

User Story:

"As a data analyst, I want to monitor response times and quality from different AI models to optimize my workflow."

Workflow:

  1. Configure multiple models with test status tracking
  2. Use different models for similar tasks
  3. Monitor response times and quality
  4. Check test status and last tested timestamps
  5. Make data-driven decisions about model selection

Benefits:

  • Performance-based model selection
  • Data-driven optimization
  • Quality assurance

User Experience Scenarios​

Scenario A: First-Time Setup​

New User Journey:

  1. Discovery: User opens chat app, sees "No LLM Selected" in header
  2. Configuration: Clicks dropdown → "Configure in Settings"
  3. Setup: Adds Google Gemini Flash 2.5 API key
  4. Testing: Tests key to ensure it works (green checkmark appears)
  5. Activation: Key automatically becomes active (first key)
  6. Usage: Returns to chat, sees "Google Gemini Flash 2.5" selected
  7. First Message: Sends message, receives real AI response

Scenario B: Power User Workflow​

Advanced User Journey:

  1. Multi-Setup: Configures 3-4 different AI providers
  2. Testing: Tests all keys, sees status indicators
  3. Task-Based Selection:
    • Morning: Selects Gemini Flash for email responses
    • Afternoon: Switches to GPT-4 for code review
    • Evening: Uses Claude for creative writing
  4. Quick Switching: Uses header dropdown for instant model changes
  5. Monitoring: Checks test status periodically

Scenario C: Error Recovery​

Error Handling Journey:

  1. Normal Usage: User chatting with Google Gemini Flash 2.5
  2. Error Occurs: API rate limit reached or service down
  3. Error Message: Chat shows: "Sorry, I encountered an error: API request failed: 429 Too Many Requests. Please check your API key configuration."
  4. Quick Recovery: User clicks header dropdown
  5. Backup Selection: Switches to OpenAI GPT-4
  6. Continued Work: Conversation continues seamlessly

Business Value Propositions​

For Individual Users​

  • Flexibility: Choose the best AI for each task
  • Cost Control: Use cheaper models when appropriate
  • Reliability: Backup options when services fail
  • Quality: Access to latest and best AI models

For Development Teams​

  • Standardization: Consistent AI access across team
  • Experimentation: Easy A/B testing of different models
  • Cost Management: Centralized billing and usage tracking
  • Productivity: Optimized model selection for different tasks

For Businesses​

  • ROI Optimization: Use cost-effective models without sacrificing quality
  • Risk Mitigation: Multiple provider relationships
  • Scalability: Easy addition of new AI providers
  • Compliance: Control over which AI services are used

Advanced Use Cases​

Use Case 8: Model Cascading​

Advanced Workflow:

  1. Start with fast, cheap model (Gemini Flash 2.5)
  2. If response is unsatisfactory, escalate to premium model (GPT-4)
  3. Use premium model insights to refine approach
  4. Return to cost-effective model for follow-up questions

Use Case 9: Specialized Conversations​

Domain-Specific Usage:

  • Medical Queries: Use Claude (known for careful, nuanced responses)
  • Code Generation: Switch to GPT-4 (strong coding capabilities)
  • Creative Writing: Use Gemini for brainstorming, Claude for refinement
  • Data Analysis: GPT-4 for complex analysis, Gemini for quick insights

Use Case 10: Geographic Optimization​

Global Usage:

  • US Users: Prefer OpenAI (lower latency)
  • EU Users: Use local providers for data compliance
  • APAC Users: Google Gemini for regional optimization
  • Automatic Selection: Based on user location or preferences

Metrics & Success Criteria​

User Engagement Metrics​

  • Model Switch Frequency: How often users change models
  • Session Duration: Time spent with different models
  • Error Recovery Rate: Success rate of switching after errors
  • Feature Adoption: Percentage of users configuring multiple models

Quality Metrics​

  • Response Satisfaction: User ratings per model
  • Task Completion Rate: Success rate for different model types
  • Error Frequency: API failures per model
  • Response Time: Average response time per provider

Business Metrics​

  • Cost Per Interaction: Average cost across different models
  • API Usage Distribution: Usage patterns across providers
  • User Retention: Impact of multi-model access on user retention
  • Support Tickets: Reduction in AI-related support requests

Future Enhancements​

Planned Features​

  1. Auto-Model Selection: AI-powered model recommendation based on query type
  2. Usage Analytics: Detailed dashboards showing model performance and costs
  3. Team Management: Shared API key pools and usage quotas
  4. Model Comparison: Side-by-side response comparison interface
  5. Custom Prompts: Model-specific prompt templates and configurations

Integration Opportunities​

  1. Calendar Integration: Schedule model usage based on task types
  2. Project Management: Link model selection to project contexts
  3. Cost Tracking: Integration with billing and expense systems
  4. Performance Monitoring: Real-time model performance dashboards

Conclusion​

The LLM selection feature transforms a simple chat application into a powerful, flexible AI workspace. By enabling users to configure, test, and switch between multiple AI models, the application serves diverse use cases from individual productivity to enterprise-scale AI operations.

The feature's strength lies in its simplicity and flexibility - users can start with a single model and gradually expand their AI toolkit as their needs evolve. Whether optimizing for cost, quality, reliability, or specialized capabilities, the LLM selection system provides the foundation for intelligent AI usage.

This implementation positions the application as a comprehensive AI interface that adapts to user needs rather than forcing users to adapt to technology limitations.