Help people find answers in the information your organisation already maintains. Brolly’s RAG chatbot combines document retrieval with AI responses. Explore the workflow, understand the architecture, and request a guided demonstration with our team.
Retrieval-augmented generation, or RAG, gives a language model relevant source material before it writes an answer. Instead of relying only on the model’s general knowledge, the assistant looks for information related to the question. Brolly’s course-information example uses Digital Brolly marketing-course documents. A business implementation begins with your own approved sources, users and access requirements.
A conversational question can be more natural than guessing the right document title. The useful starting point is a collection of questions that staff or customers ask repeatedly, together with the sources that should answer them.
A course assistant should answer course questions; an internal policy assistant should work from approved policies. A clear boundary helps users understand what the assistant can help with and when a person needs to take over.
Retrieval gives the response relevant context, but it does not guarantee correctness. Source freshness, access controls and representative answer checks are essential when deciding whether a response is ready for real users.
Identify the documents that contain approved answers. Decide who maintains them and which audiences may use their information.
The user describes what they need in natural language. A focused question helps the system look for relevant information.
The retrieval layer finds relevant passages and supplies context for the language model’s response.
Check whether the answer addresses the question and is supported by the source material. Missing or ambiguous information needs clarification or human follow-up.
Follow the functional layers from input to output. During your demo, our team can discuss the technology choices, integrations and deployment requirements for your environment.
Approved documents are prepared for retrieval. Content ownership and an update process matter as much as the initial import.
The question is matched to relevant source passages. This is the part that connects a general language model to a particular knowledge collection.
Instructions and retrieved context guide the language model when composing an answer. The response should stay within the agreed subject and access boundaries.
The chat interface presents the response. A business deployment should define source presentation, escalation, evaluation and the handling of unsupported questions.
The Brolly demonstration uses course information to illustrate questions about learning content. It shows how a bounded knowledge collection can support repeated enquiries.
A potential use is helping employees navigate approved process documents or manuals. Scope permissions carefully so the assistant does not make restricted information available to the wrong audience.
For a separately scoped deployment, a maintained help centre or product catalogue can provide source material. Decide how the assistant should hand unresolved questions to the support team.
Tell us what you would like to see and include your preferred meeting time and time zone. Our team will follow up to agree a suitable time.
RAG retrieves information from a chosen knowledge collection before generating an answer. A general chatbot may answer without looking at your organisation’s documents. The quality of a RAG answer still depends on the sources and retrieval.
No. Missing information, retrieval errors and generation errors can still lead to an incorrect answer. Evaluation and a clear response to unsupported questions are part of planning a useful assistant.
No. This page explains the application. Select Request a demo, enter your details, and our team will arrange a guided demonstration. Do not include confidential documents in the enquiry.
AI chatbot and RAG development are part of Brolly’s services. The source documents, user permissions, integrations and deployment requirements are agreed separately for your project.