Retrieval-augmented generation (RAG)
Retrieval-augmented generation (RAG) is an AI architecture that improves the accuracy of language model responses by retrieving relevant information from external data sources before generating a response. Instead of relying solely on what the model learned during training, RAG pulls current, verified information from a knowledge base or document store and uses it to ground the response.
In customer service, RAG addresses one of the biggest risks of using large language models: hallucination. Without RAG, an LLM might generate plausible-sounding but incorrect information about a company's policies, pricing, or procedures. With RAG, the LLM references the company's actual documentation when formulating responses.
The RAG process works in three steps:
Retrieval: When a customer asks a question, the system searches the knowledge base for relevant documents, articles, or data
Augmentation: The retrieved information is included in the LLM's context alongside the customer's message
Generation: The LLM generates a response grounded in the retrieved information rather than its parametric knowledge
RAG quality depends heavily on:
Knowledge base quality: RAG can only retrieve what exists. Gaps, outdated content, or conflicting articles in the knowledge base directly degrade response quality.
Retrieval accuracy: The system must find the right documents for the given question. Poor retrieval means the LLM generates responses grounded in irrelevant information.
Chunking strategy: How documents are split and indexed affects whether the retrieval system can find the specific paragraph that answers the customer's question.
For CX teams, RAG is one of those architectural decisions that's invisible when it works and obvious when it doesn't. The practical question is not whether the AI uses RAG (most do), but how well the RAG pipeline is implemented and maintained.
Related terms: knowledge base, large language model, AI hallucinations
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.