Large language model (LLM)
A large language model (LLM) is a type of AI model trained on vast amounts of text data that can understand, generate, and reason about natural language. Models like GPT-4, Claude, Gemini, and Llama are examples. LLMs are the core technology enabling the current generation of AI customer service agents.
LLMs differ from earlier NLP approaches in a fundamental way: they don't just classify or extract information from text — they can generate contextually appropriate responses, reason through multi-step problems, and handle the ambiguity inherent in natural language. This is what makes modern AI agents capable of genuine conversations rather than scripted interactions.
In customer service applications, LLMs are used for:
Conversation handling: Understanding customer messages and generating appropriate responses
Reasoning: Breaking down complex requests into steps and determining the right course of action
Knowledge retrieval: Understanding which information is relevant to a given query and synthesizing it into a coherent answer
Summarization: Condensing long conversation histories or ticket notes into concise summaries for handoffs
Key considerations for CX leaders when evaluating LLM-based AI solutions:
Accuracy vs. fluency: LLMs can generate convincing-sounding responses that are factually wrong (hallucinations). The system architecture matters more than the model choice.
Cost: LLM inference has a per-token cost that scales with conversation length and volume. Pricing models should account for this.
Latency: Response generation time varies by model size and complexity. For real-time chat, latency matters.
Data privacy: Understanding how customer data flows through LLM inference, whether conversations are used for model training, and where data is processed is critical for regulated industries.
Related terms: AI agent, AI hallucinations, natural language processing
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.