AI Production Checklist: From Demo to Product

The first seven articles built the mental model. This one is how to turn a working demo into a product.

1. Define the Problem, Not the Tech

❌ "We need RAG"
✅ "Support answers 200 repetitive product questions daily—60% of time.
    Goal: automate 80% of repeats."

Quantify first, pick tech second. Many failures solve problems that don’t exist.

2. Start with the Smallest Scenario

V1:
  - 1 data source (FAQ)
  - 1 user type (internal support)
  - 1 question category (product features)

Not V1:
  - 10 sources × 5 user types × all questions

The 20% of features serving 80% of users is your MVP.

3. Build an Eval System

No eval = no idea if you improved.

Offline:
  - 50–100 question + gold-answer pairs
  - Re-run on every prompt/model/RAG change
  - Metrics: accuracy, relevance, hallucination rate

Online:
  - thumbs up/down
  - weekly human spot-check (20 samples)
  - business metric: ticket volume down?

4. Layered Hallucination Defense

Layer 1: prompt — "answer only from provided materials; say unsure if not covered"
Layer 2: RAG citations — every answer links sources
Layer 3: confidence gate — low confidence → human
Layer 4: human review — medical, legal, finance

One prompt cannot solve hallucination—defense in depth.

5. Cost Estimation

Cost per turn ≈ (input tokens + output tokens) × price

Example (GPT-4o):
  2000 input × $2.5/1M = $0.005
  500 output  × $10/1M  = $0.005
  ≈ $0.01 per conversation

1000 DAU × 5 turns = 5000/day → ~$50/day → ~$1500/month

Optimizations:
  - small model for easy queries (10× cheaper)
  - cache frequent answers
  - RAG to shrink input tokens
  - per-user daily limits

6. Security Red Lines

Risk	Mitigation
Prompt injection	input filtering + system prompt isolation
Data leakage	no secrets in prompts; authenticated APIs
Over-permissioned agents	least privilege on tools
Harmful output	moderation APIs + filters
Vendor lock-in	backup models; don’t 100% depend on AI for critical paths

7. Monitoring and Alerts

After launch, track:

- latency (P50, P99)
- error rate (timeouts, 5xx)
- token usage (daily trend, spikes)
- user feedback (negative rate)
- hallucination detection (if automated)

8. UX for AI Products

Different from traditional software:

• Show uncertainty: “Answer based on handbook v3.2—contact support if unsure”
• Allow correction: “Was this helpful?”
• Graceful fallback: human handoff—not “Error 500”
• Streaming: long answers stream so users see progress

9. Iteration Cadence

Weeks 1–2: MVP (one scenario + basic RAG)
Weeks 3–4: feedback → prompt + retrieval tuning
Month 2:   more sources + eval harness
Month 3:   data-driven choice: fine-tune? add agent?

Don’t boil the ocean. AI products improve with usage data.

10. Team Skills

Role	Needs
Product	scenarios, eval criteria, UX
Engineering	RAG pipeline, APIs, deploy, monitor
Domain expert	test data, quality review

Minimum viable team: 1 full-stack engineer + 1 domain expert → MVP in ~2 weeks.

Full Checklist

□ Clear problem and metric
□ Small MVP scope
□ 50+ test cases
□ At least two hallucination layers
□ Cost estimate + optimization plan
□ Security review done
□ Monitoring live
□ UX fallback designed
□ Iteration plan written
□ Roles assigned

Series Recap

Eight articles from “what is AI” to production:

Concept → Mechanism → Prompt → RAG → Agent → Architecture → Multimodal → Production
 Lv.1      Lv.2       Lv.2    Lv.3   Lv.3      Lv.4         Lv.4         Lv.5

AI moves fast; these frameworks age more slowly. New models ship—you’ll still know what changed for you.

This series will keep growing as we learn.