Monitoring and AI quality control
Automation 12 min
0/21
Business problem
The first version of automation works (a perfect demo). After a week: AI generates weird emails (hallucinations), classifies 20% of replies wrongly, 3 workflows failed (API timeout), API costs grow, and 200 duplicate leads sit in the CRM. Without monitoring you discover it after 2 weeks — after sending 500 bad emails.
Proven method
Monitoring in 6 areas:
- AI QUALITY: hallucination monitoring (facts without a source — the rule: every fact in a brief has a source; a sample audit), bad outputs (a content quality score: personalization quality < 70 → human review), wrong classification (a 5% classification sample checked by a human),
- DATA: duplicate leads (real-time dedup + a report), spam (complaints, unsubscribe rate, spam reports),
- INFRA: failed workflows (how many failed, where), API failures (status, retries), API limits,
- COSTS: cost monitoring (how much you spend on AI/API daily, per lead, per campaign),
- LOGS: everything is logged (what executed, what AI returned, what a human changed) — without logs you can't debug,
- ALERTS: notifications when thresholds are exceeded (Slack/email): a workflow failed > 5x, cost > X/day, quality < 70, bounces > 3%. AI QUALITY SCORE: a quality aggregate (how often AI generates content to fix, how often classifications are correct, how often a workflow needs intervention) — goal: > 85%. Course thresholds: personalization quality < 70 → human review, lead score < 50 → don't contact, confidence < 80 → manual review. Rules: monitoring isn't a 'nice to have' — it's the condition for keeping automation running; alerts should be rare and specific (not 5 emails a day); a sample audit (5%) is cheaper than a full audit.
Market example
- BAD
- Manual, inconsistent execution or chaotic automation lacking data validation.
- GOOD
- After a week of monitoring: quality score 78 (goal 85) — AI generates openers with facts without a source in 12% of the sample (hallucination risk). Action: you strengthen the prompt (a source requirement) + a quality gate < 70 sends it back to a human. Costs: 1.2/lead (budget 0.8) — you increase batching (20% savings). Workflow: 3 fails (Gmail API timeout) — you add retries + an alert at 5. Classifications: 6% wrong (goal < 5%) — you fix the prompt + confidence thresholds. The system works — because you measure it.
Ready-to-use prompt
You are a monitoring architect. Context: I have automation: [DESCRIPTION — workflow, AI, CRM]. Budget: [X]/month on AI. Alerts: [TOOLS — SLACK/EMAIL]. Task: Design monitoring: 1. AI quality: how to measure hallucinations, bad outputs, wrong classification (metrics + samples) 2. An AI quality score: what makes up the score and what the goal is 3. Data: duplicates and spam (metrics + thresholds) 4. Infra: failed workflows, API failures, limits (how to track + retries) 5. Costs: what to measure (daily, per lead, per campaign) + thresholds 6. Logs: what to log (workflow, AI output, human changes) — a structure 7. Alerts: a threshold → alert list (a table: condition → action) — max 5 alerts a day 8. A weekly review: what I check in 30 min every week Format: 8 sections.
Model rekomendowany: Claude 3.5 Sonnet / GPT-4o Copy and paste into ChatGPT, Claude or Perplexity
ALTERNATYWNE SCENARIUSZE
Variations & alternative scenarios
Light version: logs + 3 alerts (workflow failed, cost, bounces).
Full version: a quality score + sample audits + weekly reports.
Practical exercise
Define the AI quality score (3 metrics + a goal). Write 5 thresholds → alerts (a table). Plan a 30-minute weekly review (what you check).
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