From prompts to production-grade product catalog system.
Monday: 3 core prompts. Tuesday: automation code. Wednesday: team workflows. Thursday: complete technical architecture. Agents, ML pipeline, data flows, scaling patterns, and multi-tenant deployment for enterprise e-commerce.
Key Assumptions
System Requirements
Functional
- Extract product attributes from raw data (images, specs, competitor text)
- Generate SEO-optimized descriptions in 12+ languages
- Validate outputs for brand voice, accuracy, and compliance
- Integrate with CMS, PIM, inventory, and analytics systems
- Support bulk processing (10K+ SKUs) and real-time single-SKU updates
- A/B test descriptions and track conversion impact
- Human-in-the-loop review queue for low-confidence outputs
Non-Functional (SLOs)
💰 Cost Targets: {"per_sku_usd":0.05,"monthly_infra_usd_startup":500,"monthly_infra_usd_enterprise":5000}
Agent Layer
planner
L3Decomposes SKU generation task into subtasks, selects tools
🔧 fetchProductData(), fetchCompetitorData(), selectLLM(), estimateCost()
⚡ Recovery: If data fetch fails → retry 3x with backoff, If no competitor data → use generic template, If cost exceeds budget → downgrade to cheaper LLM
executor
L2Executes generation plan, calls LLM, formats output
🔧 callLLM(), formatMarkdown(), extractKeywords(), translateText()
⚡ Recovery: If LLM timeout → retry with shorter prompt, If hallucination detected → regenerate with stricter prompt, If translation fails → fallback to English
evaluator
L3Validates output quality, checks brand voice, detects hallucinations
🔧 checkFactualAccuracy(), scoreBrandVoice(), detectHallucination(), scoreSEO()
⚡ Recovery: If low confidence (<70%) → route to human review, If hallucination detected → flag and regenerate, If brand voice mismatch → suggest edits
guardrail
L4Policy checks, PII redaction, safety filters, compliance validation
🔧 detectPII(), checkPolicyViolations(), redactSensitiveData(), validateCompliance()
⚡ Recovery: If PII detected → auto-redact and log, If policy violation → block publication and alert, If compliance fail → route to legal review
seo
L2Optimize descriptions for search engines, inject keywords
🔧 analyzeKeywordDensity(), optimizeReadability(), generateMetaTags(), scorePageRank()
⚡ Recovery: If keyword stuffing detected → rebalance, If readability too low → simplify language, If meta tags missing → auto-generate
translation
L2Translate descriptions to 12+ languages with cultural adaptation
🔧 translateText(), adaptCulturalContext(), validateGrammar(), checkLocalCompliance()
⚡ Recovery: If translation API down → queue for later, If low quality score → human translator review, If cultural mismatch → adapt phrasing
ML Layer
Feature Store
Update: Real-time for inventory, daily batch for embeddings, weekly for analytics
- • product_category_embedding
- • brand_voice_vector
- • competitor_price_stats
- • historical_conversion_rate
- • user_engagement_metrics
- • seo_keyword_relevance
- • image_quality_score
- • inventory_velocity
Model Registry
Strategy: Semantic versioning with A/B testing before promotion
- • description_generator_v3
- • brand_voice_classifier
- • hallucination_detector
- • seo_scorer
Observability Stack
Real-time monitoring, tracing & alerting
0 activeDeployment Variants
Startup Architecture
Fast to deploy, cost-efficient, scales to 100 competitors
Infrastructure
Risks & Mitigations
⚠️ LLM hallucinations publish false product claims
Medium✓ Mitigation: 4-layer detection: confidence scores → spec cross-check → GPT-4 fact-check → human review. Block publication if any layer fails.
⚠️ Cost overruns from excessive LLM API usage
High✓ Mitigation: Set hard budget limits ($0.10/SKU max), downgrade to cheaper models if exceeded, alert ops team at 80% threshold.
⚠️ Data breach exposes customer PII or payment data
Low✓ Mitigation: Encrypt all data at rest (AES-256), in transit (TLS 1.3), redact PII before LLM processing, audit logs for 7 years, annual penetration testing.
⚠️ CMS integration breaks after platform update
Medium✓ Mitigation: Version-locked SDKs, adapter pattern for multi-CMS support, automated integration tests, fallback to manual queue if API fails.
⚠️ Model drift degrades quality over time
High✓ Mitigation: Monitor quality metrics weekly (rolling 7-day window), alert if accuracy drops >3%, auto-rollback to previous model version, quarterly retraining.
⚠️ Vendor lock-in to single LLM provider
Medium✓ Mitigation: Multi-provider strategy (Claude + GPT), abstraction layer for easy switching, test failover monthly, negotiate volume discounts.
⚠️ Compliance violations (PCI, GDPR, CCPA)
Low✓ Mitigation: Annual compliance audits (SOC 2, PCI-DSS), data residency controls, automated PII redaction, legal review of all policies, incident response plan.
Evolution Roadmap
Progressive transformation from MVP to scale
Phase 1: MVP (0-3 months)
Phase 2: Scale (3-6 months)
Phase 3: Enterprise (6-12 months)
Complete Systems Architecture
9-layer architecture from presentation to security
Presentation
4 components
API Gateway
4 components
Agent Layer
6 components
ML Layer
5 components
Integration
4 components
Data
4 components
External
4 components
Observability
4 components
Security
4 components
Request Flow - Single SKU Description Generation
Automated data flow every hour
Data Flow - End-to-End
From product data ingestion to CMS publication
Key Integrations
CMS (Shopify/Magento)
PIM (Product Information Management)
Inventory System
Analytics (Google Analytics 4)
Payment Gateway (Stripe)
Security & Compliance
Failure Modes & Fallbacks
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| LLM API down (Claude/GPT) | Switch to backup LLM (GPT → Claude or vice versa) | Slight quality variance, 10% slower | 99.9% |
| Low-confidence generation (<70% quality score) | Route to human review queue | Delayed publication (1-4 hours) | 100% accuracy maintained |
| Hallucination detected | Block publication, regenerate with stricter prompt | 2x latency for affected SKU | <1% hallucination rate |
| CMS API timeout (Shopify/Magento) | Retry 3x with backoff, then queue for later | Delayed publication (up to 15min) | 99.5% |
| Database unavailable (PostgreSQL) | Switch to read replica for reads, queue writes | Read-only mode, write latency +5min | 99.9% |
| PII detected in output | Auto-redact, log incident, block publication | Regeneration required | 100% PII protection |
| Cost budget exceeded (>$0.10/SKU) | Downgrade to cheaper LLM or use template | Lower quality, faster generation | Cost control maintained |
RAG vs Fine-Tuning
Hallucination Detection
Evaluation Framework
Dataset Curation
Agentic RAG
Model Distillation
Tech Stack Summary
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