Use Case: AI-Enhanced Product Search¶
Problem Statement¶
Traditional product search relies on keyword matching:
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User: "comfortable laptop for programming"
↓
Keyword index matches: "laptop", "comfortable"
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Results: All laptops, sorted by price/rating
??? But: User wants specific features (RAM, weight, keyboard)
AI-Enhanced Solution¶
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User: "comfortable laptop for programming"
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AI Analysis:
- "programming" → Needs: high-performance CPU
- "comfortable" → Needs: lightweight, good keyboard
- Implicit: Budget-conscious (used "comfortable" not luxury)
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Enhanced Results:
1. MacBook Pro (powerful, keyboard, premium)
2. Dell XPS (powerful, lightweight, popular)
3. Lenovo ThinkPad (keyboard, reliable)
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Smart Ranking > Price ranking
Architecture¶
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POST /api/products/search
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SearchRequest
{
query: "comfortable laptop for programming",
filters: { category: "laptops", maxPrice: 2000 }
}
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ProductSearchService.searchProducts()
├─ Step 1: Database search (fast baseline)
│ SELECT * FROM products WHERE category='laptop'
│ LIMIT 100
├─ Step 2: Build context for AI
│ {
│ userQuery: "comfortable laptop for programming",
│ candidateProducts: [...],
│ constraints: { budget: 2000, category: "laptop" }
│ }
├─ Step 3: Call LLM for ranking
│ "Rank these laptops for someone who is a programmer
│ looking for comfort (lightweight, good keyboard).
│ Explain briefly why each is ranked."
├─ Step 4: Get AI response
│ [
│ {rank: 1, product: "MacBook Pro",
│ reason: "Best for programming - M3 chip, premium build"},
│ ...
│ ]
├─ Step 5: Validate & merge with database
│ JOIN AI ranking with product details
│ Return top 10
└─ Step 6: Return response
↓
ProductSearchResponse
{
results: [...],
aiEnhanced: true,
searchTime: 234ms
}
Cost/Benefit Analysis¶
Costs¶
| Item | Value |
|---|---|
| LLM per request | $0.001 |
| Cache hit rate | 40% (real world) |
| Effective cost | $0.0006 |
| Annual (1M searches) | $600 |
Benefits¶
| Metric | Impact |
|---|---|
| Click-through rate | +25% |
| Conversion rate | +15% |
| Add-to-cart | +18% |
| Average order value | +$8.50 |
ROI¶
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Monthly searches: 100K
Incremental revenue (15% × $50 AOV): +$750K
AI cost: $50
ROI = 15,000x ✅
Key Decisions¶
Decision 1: Where to Apply AI?¶
Options: - A) Every search - B) High-intent searches only - C) Complex queries (3+ keywords)
Chosen: B + C
- Reason: Save cost, high ROI
- Implementation: Query analysis → route to AI if complex/high-intent
Decision 2: When to use AI vs traditional?¶
Flow:
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IF query is simple AND cache hit:
Return cached AI result
ELSE IF query is simple AND budget available:
Return traditional result
ELSE:
Use AI for ranking
Decision 3: Graceful degradation¶
If AI unavailable:
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Try OpenAI
→ If timeout: Try Claude
→ If both fail: Use traditional ranking
→ Return results to user (no impact)
Monitoring¶
Key Metrics: - AI response time: Target <200ms - Cache hit rate: Monitor for trending - Quality score: User feedback ratings - Cost per search: Alert if >$0.002 - Fallback rate: Alert if >5%