AI in Production: When Hallucinations Cost Money
I've learned the hard way: LLMs in business apps need verification layers or they'll destroy trust fast.
I integrated Claude into an ERPNext module last year. Looked simple: extract vendor data, validate terms, suggest payment schedules. The AI was accurate 85% of the time.
85% is catastrophic for accounting.
One hallucinated invoice amount cost a client 3 hours of audit trail debugging. Another suggested a payment date that violated contract terms. The LLM wasn't lying maliciously—it was just... confident about things it didn't actually know.
That's when I learned: never ship AI output directly to users. Especially in finance, ERP, or anywhere decisions have consequences.
Here's my pattern now:
1. Verification layer is mandatory. Before Claude's output touches the database, I run it through deterministic checks. For ERPNext integrations, that means SQL queries against actual data. For Telegram bots, I cross-reference against APIs. The AI suggests, the system verifies.
2. Confidence scores aren't trust. High probability from the model means nothing. I only act on structured data I can prove. If Claude says "vendor ABC has 30-day terms," I check the contract field first. If it's empty, the suggestion becomes a draft for human review—not an auto-filled form.
3. Human handoff is cheap insurance. On open source products like Seven Suite, I've built AI-assisted features that flag uncertain outputs for review. Users see "AI suggested X, but verify before saving." Takes 2 minutes to implement. Prevents disasters.
4. Log everything. When something goes wrong (and it will), you need the audit trail. What prompt did we send? What was the exact output? When did a human override it? I store all of this. Saved me twice already.
The companies getting AI right in production aren't the ones trusting models blindly. They're the ones treating LLM outputs like user input—suspicious until proven safe.
For Telegram bots or quick internal tools? Fine, move fast. But anything touching money, compliance, or customer data? Build the verification layer first. The AI is the assistant. You're still in charge.
Trust the system. Never trust the hallucination.