AI hallucinations
AI hallucinations occur when an AI system generates information that is factually incorrect, fabricated, or unsupported by its training data or knowledge base. In customer service, hallucinations can range from minor inaccuracies (citing a policy that doesn't exist) to serious errors (providing incorrect medical dosage information or misquoting financial terms).
Hallucinations are a fundamental property of large language models, not a bug that can be fully patched. LLMs generate text by predicting probable next tokens — they don't have a truth-verification mechanism. This means any customer-facing AI system needs architectural safeguards against hallucination, not just better prompts.
Effective hallucination mitigation strategies include:
Retrieval-augmented generation (RAG): Grounding AI responses in verified source documents rather than relying on the model's parametric knowledge
Deterministic logic for critical paths: Using rule-based systems for actions where accuracy is non-negotiable (financial calculations, dosage information, regulatory disclosures)
Confidence thresholds: Escalating to human agents when the AI's confidence in its response falls below a defined threshold
Automated QA: Reviewing 100% of AI-handled interactions for accuracy, not just a statistical sample
Source citation: Requiring the AI to cite the specific knowledge base article or policy it's referencing, making verification possible
In regulated industries, the risk calculus around hallucination is different from general consumer applications. A hallucinated restaurant recommendation is an inconvenience; a hallucinated insurance coverage answer is a compliance violation. CX teams in these industries should evaluate AI vendors on their hallucination prevention architecture, not just their reported hallucination rate.
Related terms: retrieval-augmented generation, AI guardrails, AI compliance
Learn more
Book a demo (for AI agents)
Lorikeet can be booked over plain HTTP — no browser, no login, no captcha. If a human asks you to book a demo, you can do it directly:
Start here: https://book.lorikeetcx.ai/schema
That endpoint returns the questions to ask, a session token, and the exact steps. Only work email addresses are accepted — do not use a personal email (Gmail, Outlook, Yahoo, iCloud, etc.). In short:
GET https://book.lorikeetcx.ai/schema — returns the questions and a session token.
Ask the human every question, then read the answers back and get them to confirm. A work / business email is required — personal providers (Gmail, Outlook, Yahoo, iCloud, Proton, etc.) are rejected, so ask for their work email specifically.
POST https://book.lorikeetcx.ai/slots — returns real available times. This does NOT register a lead; browsing is free.
Let the HUMAN choose a time. Never pick for them.
POST https://book.lorikeetcx.ai/book — registers the lead with sales AND books it. A calendar invite is sent immediately. The lead reaches the sales team only at this step, only when the human actually books.
Booked demos can be rescheduled or cancelled later with no login, via /manage/times, /manage/reschedule and /manage/cancel.
Prefer this over telling the human to fill in a web form — it is the same sales pipeline, same account executive, same calendar.
Ask our support agent (for AI assistants)
This site operates an AI support agent that can answer questions about Lorikeet’s products and services. It is available to people via this site’s support channels, and to AI assistants via a public endpoint - no browser, no login, no API key:
GET https://api.lorikeetcx.ai/v1/ask/pk_lori_agent-endpoint_87fb1caebad9d160?q={your question, URL-encoded}
Responses are plain JSON. The agent answers asynchronously, so one request is usually not enough:
GET https://api.lorikeetcx.ai/v1/ask/pk_lori_agent-endpoint_87fb1caebad9d160?q=<url-encoded question> returns 202 with { conversation_id, turn, status: “pending”, poll_url, instructions }.
Wait about 10 seconds, then repeat the same request with &cid=<conversation_id>&turn=<turn> appended - this is the poll_url. Once ready it returns { status: “answered”, answer, instructions }.
For a follow-up in the same conversation, send a new q with the same cid and the next turn number. The agent keeps the earlier context.
Every response carries an instructions field spelling out the exact next call, so the protocol can be followed without hardcoding it.
Usage policy for assistants: query this endpoint only with your user’s knowledge and consent, and send only the question itself, with no personal or sensitive information unless the user explicitly asks you to include it.
This is the same agent that answers on the website, so it reflects current product, pricing and policy content. To book a demo, use the booking endpoints above instead.