Question

Imagine you are a project manager at a tech startup tasked with integrating an AI language model like ChatGPT into your company's customer service platform. While testing, you notice the model occasionally generates incorrect information. What is the most suitable action to take?

Answer 1

Short answer: Do not deploy the model to handle customer queries autonomously until you mitigate the hallucination risk. Put human oversight and technical guardrails in place, fix the root causes you can, and add monitoring and escalation so incorrect outputs can’t reach or harm customers.

Concrete, prioritized actions

1. Immediate (hours)
- Pause autonomous deployment or restrict the model from answering critical/ high-risk topics (billing, account changes, legal, medical).
- Require human-in-the-loop (HITL) for all or a subset of responses (eg. low-confidence answers, high-risk categories).
- Add a safety system prompt instructing the model to be conservative: “If you are unsure or cannot verify, say ‘I don’t know’ and escalate to a human.”
- Label AI answers clearly and provide an easy “contact human agent” option.

2. Short term (days–weeks)
- Use Retrieval-Augmented Generation (RAG): only let the model answer after consulting your verified knowledge base (KB, FAQ, ticket history). Force the model to cite which source it used.
- Implement a verification layer:
- Simple rules: for answers containing account-specific or irreversible actions, block and escalate.
- Automated fact-checker: run a lightweight model or checker that compares the LLM output to KB results and flags mismatches.
- Confidence threshold: if the model’s confidence (or the verifier’s score) is below threshold, route to human.
- Create a test suite for hallucination-prone queries and run regression tests before releases.
- Log all AI responses, inputs, model states and verifier decisions for auditing and debugging.

3. Medium term (weeks–months)
- Fine-tune or instruct-tune the model using curated, domain-specific QA pairs and high-quality examples from your KB and support transcripts.
- Build metrics and monitoring dashboards: hallucination rate, accuracy on canonical Qs, escalation rate, resolution time, customer satisfaction.
- Implement continuous feedback loop: allow agents and customers to flag incorrect answers; use that data to retrain/fix KB.
- Improve prompts and system messages iteratively, A/B test prompt variants.

4. Long term / product-level
- Define policies for allowable automated responses vs. human-only responses by risk profile.
- Consider hybrid architectures: deterministic template responses for transactional flows and LLM for conversational assistance.
- Engage vendor/provider: report hallucination patterns, ask about model updates, safety options, and configure model temperature, system-level constraints, or use smaller retrieval-only models for facts.
- Ensure compliance and privacy: don’t expose PII to external models without consent and review data handling.

Practical implementation pattern (example)
User query -> Query KB/search -> If KB has high-confidence answer, return templated answer (optionally paraphrased by LLM with citation). If KB low confidence -> call LLM with system prompt + retrieved context -> run verifier comparing LLM output vs retrieved docs -> if verifier score high -> send to customer with citation; else -> escalate to human agent.

Monitoring and KPIs to watch
- Hallucination rate (incorrect answers / total AI answers)
- Escalation rate and reason codes
- Time-to-human-hand-off
- Customer satisfaction post-AI interaction
- False-blocking rate (useful responses blocked)

When to contact the model provider
- If errors suggest model regression or specific failure modes across many prompts, open a support ticket with examples and logs.
- Ask about available safer/factual variants, tools for grounding (retrieval integrations), or options to tune behavior.

Summary recommendation
Don’t accept occasional incorrect outputs as “normal” for production without controls. Immediately restrict/require human oversight and add retrieval + verification layers, logging, and a feedback loop. Then iteratively reduce hallucinations by fine-tuning, stronger grounding, and monitoring until the system meets your safety and accuracy targets for customer-facing use.