# Model Configuration ### Overview Model configuration allows you to customize how AI models behave in AINexLayer. You can adjust parameters like temperature, token limits, system prompts, and model selection to achieve the best results for your specific needs. ### Configuration Levels #### Global Configuration * **Default Models**: Set default models for new workspaces * **Fallback Models**: Configure fallback models for reliability * **Global Settings**: Apply settings across all workspaces * **System Prompts**: Define default system behavior

#### Workspace Configuration * **Model Selection**: Choose specific models per workspace * **Custom Prompts**: Tailor AI behavior for specific use cases * **Temperature Settings**: Control creativity and consistency * **Token Limits**: Set response length limits

#### Chat Configuration * **Per-Conversation**: Override settings for specific chats * **Dynamic Adjustment**: Change settings during conversations * **Context-Specific**: Adapt to different types of queries * **User Preferences**: Remember user preferences

### Model Parameters #### Temperature Controls the randomness and creativity of responses. **Values and Effects** * **0.0**: Deterministic, consistent responses * **0.3**: Slightly creative, mostly consistent * **0.7**: Balanced creativity and consistency (default) * **1.0**: Highly creative, varied responses **Configuration** ```json { "temperature": 0.7, "description": "Balanced creativity and consistency" } ``` **Use Cases** * **0.0-0.3**: Factual queries, technical documentation * **0.4-0.7**: General conversation, analysis * **0.8-1.0**: Creative writing, brainstorming #### Max Tokens Limits the maximum length of AI responses. **Configuration** ```json { "maxTokens": 2000, "description": "Limit responses to 2000 tokens" } ``` **Guidelines** * **500-1000**: Short, concise responses * **1000-2000**: Standard responses (default) * **2000-4000**: Detailed, comprehensive responses * **4000+**: Very long, in-depth responses #### Top P (Nucleus Sampling) Controls diversity by limiting token selection to the most likely tokens. **Configuration** ```json { "topP": 0.9, "description": "Use top 90% of probability mass" } ``` **Values and Effects** * **0.1**: Very focused, predictable responses * **0.5**: Moderately focused responses * **0.9**: Balanced diversity (default) * **1.0**: Maximum diversity #### Frequency Penalty Reduces repetition by penalizing frequently used tokens. **Configuration** ```json { "frequencyPenalty": 0.0, "description": "No frequency penalty" } ``` **Values and Effects** * **-2.0**: Encourage repetition * **0.0**: No penalty (default) * **1.0**: Moderate penalty * **2.0**: Strong penalty against repetition #### Presence Penalty Encourages new topics by penalizing tokens that have already appeared. **Configuration** ```json { "presencePenalty": 0.0, "description": "No presence penalty" } ``` **Values and Effects** * **-2.0**: Encourage staying on topic * **0.0**: No penalty (default) * **1.0**: Moderate penalty * **2.0**: Strong penalty, encourages new topics ### System Prompts #### Default System Prompt ```markdown You are a helpful AI assistant that specializes in answering questions about the documents in this workspace. When responding: 1. Always base your answers on the provided documents 2. Cite specific sources when possible 3. If you cannot find relevant information, say so clearly 4. Provide practical, actionable advice 5. Maintain a professional but approachable tone ``` #### Custom System Prompts ```markdown You are a technical documentation assistant for a software development team. Your role: - Explain technical concepts clearly - Provide code examples when relevant - Suggest best practices - Identify potential issues - Help with troubleshooting Guidelines: - Use clear, concise language - Include relevant code snippets - Reference official documentation - Suggest next steps - Ask clarifying questions when needed ``` #### Role-Based Prompts ```markdown You are a customer support specialist with access to our knowledge base. Your responsibilities: - Answer customer questions accurately - Provide step-by-step solutions - Escalate complex issues appropriately - Maintain a helpful, professional tone - Follow company policies and procedures Remember: - Always be polite and patient - Provide clear, actionable solutions - Ask for clarification when needed - Document interactions appropriately ``` ### Model Selection Strategies #### By Use Case **Technical Documentation** ```json { "model": "gpt-4", "temperature": 0.3, "maxTokens": 2000, "systemPrompt": "You are a technical documentation expert..." } ``` **Creative Writing** ```json { "model": "gpt-4", "temperature": 0.8, "maxTokens": 3000, "systemPrompt": "You are a creative writing assistant..." } ``` **Data Analysis** ```json { "model": "claude-3-sonnet", "temperature": 0.1, "maxTokens": 4000, "systemPrompt": "You are a data analysis expert..." } ``` **Customer Support** ```json { "model": "gpt-3.5-turbo", "temperature": 0.5, "maxTokens": 1500, "systemPrompt": "You are a customer support specialist..." } ``` #### By Performance Requirements **Speed Priority** ```json { "model": "gpt-3.5-turbo", "temperature": 0.7, "maxTokens": 1000, "topP": 0.9 } ``` **Quality Priority** ```json { "model": "gpt-4", "temperature": 0.3, "maxTokens": 3000, "topP": 0.8 } ``` **Cost Priority** ```json { "model": "gpt-3.5-turbo", "temperature": 0.7, "maxTokens": 1000, "frequencyPenalty": 0.1 } ``` ### Configuration Management #### Environment Variables ```bash # Default model settings DEFAULT_LLM_MODEL=gpt-4 DEFAULT_TEMPERATURE=0.7 DEFAULT_MAX_TOKENS=2000 # Model-specific settings OPENAI_TEMPERATURE=0.7 ANTHROPIC_TEMPERATURE=0.7 GOOGLE_TEMPERATURE=0.7 # System prompts DEFAULT_SYSTEM_PROMPT="You are a helpful AI assistant..." TECHNICAL_SYSTEM_PROMPT="You are a technical expert..." CREATIVE_SYSTEM_PROMPT="You are a creative writing assistant..." ``` #### Configuration Files ```json { "models": { "default": { "provider": "openai", "model": "gpt-4", "temperature": 0.7, "maxTokens": 2000, "topP": 0.9, "frequencyPenalty": 0.0, "presencePenalty": 0.0 }, "technical": { "provider": "openai", "model": "gpt-4", "temperature": 0.3, "maxTokens": 3000, "systemPrompt": "You are a technical documentation expert..." }, "creative": { "provider": "openai", "model": "gpt-4", "temperature": 0.8, "maxTokens": 4000, "systemPrompt": "You are a creative writing assistant..." } } } ``` ### Dynamic Configuration #### Context-Aware Configuration ```json { "contextRules": [ { "condition": "queryType == 'technical'", "config": { "temperature": 0.3, "systemPrompt": "You are a technical expert..." } }, { "condition": "queryType == 'creative'", "config": { "temperature": 0.8, "systemPrompt": "You are a creative assistant..." } } ] } ``` #### User Preference Configuration ```json { "userPreferences": { "userId": "user123", "preferences": { "temperature": 0.5, "maxTokens": 1500, "responseStyle": "concise", "language": "en" } } } ``` #### A/B Testing Configuration ```json { "abTesting": { "experimentId": "exp001", "variants": [ { "name": "control", "weight": 0.5, "config": { "temperature": 0.7, "systemPrompt": "Default prompt..." } }, { "name": "variant", "weight": 0.5, "config": { "temperature": 0.5, "systemPrompt": "Optimized prompt..." } } ] } } ``` ### Performance Optimization #### Response Time Optimization ```json { "optimization": { "responseTime": { "maxTokens": 1000, "temperature": 0.7, "topP": 0.9, "streaming": true } } } ``` #### Cost Optimization ```json { "optimization": { "cost": { "model": "gpt-3.5-turbo", "maxTokens": 1000, "temperature": 0.7, "frequencyPenalty": 0.1 } } } ``` #### Quality Optimization ```json { "optimization": { "quality": { "model": "gpt-4", "temperature": 0.3, "maxTokens": 3000, "topP": 0.8, "systemPrompt": "Detailed, accurate responses..." } } } ``` ### Monitoring and Analytics #### Configuration Metrics ```json { "metrics": { "responseTime": "average", "tokenUsage": "total", "cost": "perRequest", "quality": "userRating", "satisfaction": "feedbackScore" } } ``` #### Performance Tracking ```json { "tracking": { "configurations": [ { "name": "technical", "usage": 150, "avgResponseTime": 2.3, "avgCost": 0.05, "qualityScore": 4.2 } ] } } ``` ### Troubleshooting #### Common Issues **Poor Response Quality** * **Temperature Too High**: Reduce temperature for more focused responses * **Inappropriate System Prompt**: Refine system prompt for better guidance * **Token Limits**: Increase max tokens for more detailed responses * **Model Selection**: Try different models for better performance **Slow Response Times** * **Model Selection**: Use faster models (GPT-3.5 vs GPT-4) * **Token Limits**: Reduce max tokens for shorter responses * **Streaming**: Enable streaming for better perceived performance * **Caching**: Implement response caching for repeated queries **High Costs** * **Model Selection**: Use cost-effective models * **Token Limits**: Reduce max tokens * **Frequency Penalty**: Use frequency penalty to reduce repetition * **Caching**: Cache responses to avoid repeated API calls #### Configuration Validation ```json { "validation": { "temperature": { "min": 0.0, "max": 2.0, "default": 0.7 }, "maxTokens": { "min": 1, "max": 4000, "default": 2000 }, "topP": { "min": 0.0, "max": 1.0, "default": 0.9 } } } ``` ### Best Practices #### Configuration Strategy * **Start with Defaults**: Begin with proven default configurations * **Test Incrementally**: Make small changes and test results * **Monitor Performance**: Track key metrics and user feedback * **Document Changes**: Keep records of configuration changes #### Model Selection * **Match Use Case**: Choose models appropriate for your use case * **Consider Costs**: Balance performance with cost * **Plan for Scale**: Consider scaling requirements * **Have Fallbacks**: Configure fallback models for reliability #### System Prompts * **Be Specific**: Clearly define the AI's role and behavior * **Include Examples**: Provide examples of good responses * **Set Boundaries**: Define what the AI should and shouldn't do * **Test and Refine**: Continuously improve prompts based on results *** **⚙️ Proper model configuration is key to getting the best results from AINexLayer. Experiment with different settings to find what works best for your specific use cases.** --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://doc.ainexlayer.com/documentation/ai-engine-configuration/model-configuration.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.