Advanced Features

RAG optimizations, caching strategies, and performance enhancements

🎯 RAG System Optimization

Vector Search Optimization

  • →
    Optimal topK Selection: Using topK=3 balances context quality and response time. More results increase context but also processing time.
  • →
    Embedding Model: text-embedding-3-small (1536 dimensions) provides excellent semantic search with fast retrieval.
  • →
    Metadata Filtering: Including metadata in queries enables category-specific searches and improves relevance.

Context Engineering

// Optimized prompt structure
const prompt = `Based on the following information...

Your Information:
${context}

Question: ${question}

Provide a helpful, professional response:`

Clear prompt structure improves LLM response quality and reduces hallucinations

⚡ Performance Enhancements

Response Time Optimization

✓ Implemented
  • • Parallel API calls (vector + LLM)
  • • Efficient data serialization
  • • Minimal DOM manipulations
  • • Optimized bundle size
💡 Future Enhancements
  • • Response streaming
  • • Edge caching with CDN
  • • Connection pooling
  • • Request batching

Caching Strategy

Client-Side Caching

React state management caches responses during user session, reducing redundant API calls

Metrics Caching

In-memory metrics storage avoids database overhead, with optional Redis integration for production

Vector Cache

Upstash Vector automatically caches frequently accessed embeddings at the database level

🔬 Advanced RAG Techniques

Hybrid Search (Future Enhancement)

Combine semantic search with keyword matching for improved retrieval accuracy

// Hybrid search implementation
const semanticResults = await vectorSearch(query);
const keywordResults = await keywordSearch(query);
const results = mergeAndRank(semanticResults, keywordResults);

Re-ranking Strategy

Current implementation uses Upstash Vector's cosine similarity scores. Future enhancements could include:

  • •Cross-encoder re-ranking for higher precision
  • •Diversity-based ranking to reduce redundancy
  • •Temporal relevance weighting for recent information

Query Expansion

Enhance user queries with synonyms and related terms before vector search:

"What is your experience?" →
"professional experience work history background employment"

🤖 LLM Optimization

Model Selection

ModelSpeedQualityUse Case
llama-3.1-8b-instant⚡⚡⚡⭐⭐⭐Current (fast responses)
llama-3.3-70b-versatile⚡⚡⭐⭐⭐⭐⭐Complex queries
mixtral-8x7b⚡⚡⭐⭐⭐⭐Alternative option

Prompt Engineering Best Practices

  • ✓
    Clear Instructions: System message explicitly defines the AI's role and constraints
  • ✓
    Context Boundaries: Separated context from user question for better parsing
  • ✓
    Temperature Control: Set to 0.7 for balanced creativity and accuracy
  • ✓
    Token Limits: Max 500 tokens ensures concise, focused responses

📊 Advanced Monitoring

Performance Metrics

P95 Latency
Response time for 95% of requests
Cache Hit Rate
Percentage of cached responses
Error Rate
Failed requests per total

Quality Metrics (Future)

Planned Enhancements:

  • • Response relevance scoring
  • • User feedback collection (thumbs up/down)
  • • Answer accuracy tracking
  • • Context retrieval precision metrics
  • • A/B testing framework for prompt variations