Assistant performance optimization requires a holistic approach that considers response quality, processing speed, resource consumption, and overall user experience.
Factors affecting performance
Knowledge base size
- Positive impact: More content = more complete and informative responses
- Negative impact: More content = longer search times
- Optimization: Keep only high-quality and up-to-date content
Content quality
- Well-structured content: Improves search speed
- Redundant content: Slows system without adding value
- Obsolete content: Creates confusion and imprecise responses
Parameter configuration
- Number of results: More results = more processing time
- Relevance threshold: Optimal threshold = less processing of irrelevant content
Optimization strategies
Content optimization
Periodic cleaning:
- Remove obsolete documents every 3-6 months
- Eliminate duplicate or very similar content
- Update frequently changing information
- Consolidate fragmented documents on similar topics
Effective structuring:
- Use clear titles and subtitles
- Organize content in logical sections
- Use bullet points for serial information
- Avoid "wall of text" without structure
Language quality:
- Use consistent terminology across all documents
- Include synonyms and keyword variations
- Write clearly and directly
- Avoid unnecessary technical jargon
Search parameter optimization
Balanced configuration for performance:
| Use Case | Num. Results | Relevance Threshold | Performance |
|---|---|---|---|
| Simple FAQ | 5-7 | 0.80-0.85 | Excellent |
| Standard support | 8-12 | 0.70-0.75 | Good |
| Complex consulting | 15-20 | 0.60-0.70 | Moderate |
Load management
Resource distribution:
- Peak hours: Consider more conservative configurations
- Smart crawling: Schedule updates during low traffic hours
- Local cache: Leverage cache mechanisms for frequent questions
Performance monitoring
Key metrics to monitor
- Average response time: Target < 3 seconds
- Search success rate: % of questions with answer found
- Token consumption: Costs per conversation
- User satisfaction: Qualitative feedback
Analysis tools
- Conversation statistics: Analyze usage patterns
- Crawler reports: Monitor scanning efficiency
- Error logs: Identify recurring problems
Scenario-specific optimizations
Scenario: High volume of requests
Typical problems:
- Long response times
- High token costs
- Possible timeouts
Solutions:
- Reduce maximum results to 8-10
- Increase relevance threshold to 0.75-0.80
- Optimize content by removing redundancies
- Implement FAQ for most common questions
Scenario: Frequent complex questions
Typical problems:
- Incomplete responses
- Lack of context
- Fragmented information
Solutions:
- Increase results to 15-18
- Reduce relevance threshold to 0.65-0.70
- Create comprehensive documents for complex topics
- Improve thematic content organization
Scenario: Limited budget
Typical problems:
- High token costs
- Need to contain usage
Solutions:
- Keep results low (6-8)
- Use high relevance threshold (0.80+)
- Focus content only on essential topics
- Implement template responses for common questions
Best practices for continuous optimization
Improvement cycle
- Measurement (weekly): Collect performance metrics
- Analysis (monthly): Identify patterns and problems
- Optimization (quarterly): Implement improvements
- Validation (continuous): Verify effectiveness of changes
Change documentation
- Track all parameter changes
- Document results of each optimization
- Maintain performance history
- Share best practices with team
Indicators of optimization need
Warning signals
- Response times > 5 seconds consistently
- Significant increase in monthly costs
- Recurring negative user feedback
- High rate of unanswered questions (>15%)
Improvement opportunities
- New content added to knowledge base
- Changes in user question patterns
- OpenAI platform updates
- Business expansion or new services