Digital Twin
Query Processing & Performance Optimization
🚀 Query Processing Optimization
Performance Improvements Summary
Significant enhancements to vector retrieval, chunking strategy, and response generation have resulted in measurable performance gains across all query types.
📊 Before vs After Comparison
❌Initial Implementation
- •Chunk Count: 15 chunks (1 per experience)
- •Retrieval Strategy: Experience-level chunking
- •Vector Search: Basic semantic similarity
- •Issue: Company name queries failed to retrieve specific achievements
- •Context Quality: Mixed results, less granular
✅Optimized Architecture
- ✓Chunk Count: 22 chunks (achievement-level)
- ✓Retrieval Strategy: STAR achievement-level chunking
- ✓Vector Search: Optimized with topK: 5 semantic search
- ✓Success: Content-based queries return precise achievements
- ✓Context Quality: Highly relevant, granular results
🎯 Key Optimization Strategies
1. Enhanced Chunking Strategy
Implementation: Changed from 1 chunk per work experience to separate chunks for each STAR achievement.
Before: exp_0 (entire CONIFS Global experience)
After: exp_0_overview + exp_0_achievement_0 + exp_0_achievement_1 + ...
Impact: 47% increase in chunk count (15 → 22) enabling more precise retrieval of specific achievements.
2. Semantic Search Optimization
Configuration: Upstash Vector with all-mpnet-base-v2 embeddings (768 dimensions).
- →
topK: 5- Retrieve top 5 most relevant chunks per query - →Semantic similarity matching for content-based queries (e.g., "database optimization", "T-SQL")
- →Index optimization for fast retrieval (<200ms average response time)
3. LLM Response Generation
Model: Groq AI with llama-3.3-70b-versatile (ultra-fast inference).
- • Temperature: 0.7 (balanced creativity and consistency)
- • Max Tokens: 1000 (comprehensive yet concise responses)
- • Context Window: Top 5 relevant chunks + query
- • Inference Speed: ~500-800ms per response
Result: Natural, context-aware responses that accurately represent professional experience.
4. STAR Methodology Integration
Approach: All professional experiences restructured using Situation-Task-Action-Result format.
Example: CONIFS Global
S: Legacy queries slow (4.2s)
T: Optimize for <2s response
A: Refactored T-SQL with indexing
R: 65% improvement (4.2s → 1.5s)
Example: Acentura
S: Legacy system slow (8s pages)
T: Modernize to <4s load time
A: ASP.NET Core migration
R: 50% improvement, 23→7 incidents
Benefit: Responses automatically include quantified results and structured narratives.
📈 Measurable Performance Gains
Chunk Granularity
15 → 22 chunks
Query Accuracy
Content-based retrieval
Response Time
End-to-end query
🧪 Testing & Validation
Comprehensive testing performed across 24+ interview-style queries with measurable improvements:
- ✓Technical Questions: Accurate retrieval of database optimization, T-SQL, Azure experience
- ✓Behavioral Questions: STAR-formatted responses with quantified achievements
- ✓Salary/Location: Consistent $80K-$100K AUD expectations with geographic preferences
- ✓Interview Simulation: PASS (HR), 7.2/10 (Technical), 8.2/10 (Hiring Manager)