What does RAG mean ? What do we use it for?

Retrieval-Augmented Generation (RAG) is a method that enables artificial intelligence language models to retrieve information from a specific knowledge base before generating a response. While standard language models are limited to the information in their training data, RAG overcomes this restriction. As shown in the diagram, when a user asks a question (Prompt), the system doesn't immediately generate a response (Response). Instead, it first finds and retrieves the most relevant information related to the question from a private document store. Then, it combines these retrieved documents with the user's original question and provides them to the main generator model (Generator).

As a result, the model creates a much more reliable, accurate, and contextually appropriate response, based not just on its own memory, but on the current and verified information provided to it. In short, RAG is a hybrid system that, when answering your question, combines the general knowledge of the AI model with the information from your own private documents, thus producing answers with much higher accuracy.

Use Cases for Customer-Facing Intelligence

1. AI Chatbot for Answering Client Questions

   Clients often have recurring questions about services, policies, timelines, or available options, which burden human support teams.

   

   Solution:
   A RAG-enabled chatbot integrates with sales documentation, FAQs, and policy documents to respond to customer questions with accurate, contextual answers.

   Value:
   Improves customer satisfaction through 24/7 assistance.
   Reduces response latency and support volume.
   Maintains message consistency across all client interactions.

   
   

Use Cases for Technical Documentation & Support

1. Smart Product Documentation Assistant

   Engineers and sales teams often struggle to locate the correct versions of technical documents, such as installation guides, certifications, or performance specs.

   

   Solution:
   A RAG-based assistant connects to internal PDFs and manuals, allowing users to ask natural language questions like “What is the tensile load of bracket B?” The system retrieves the relevant documentation and responds with verified, cited answers.

   Value:
   Accelerates field operations and sales conversations.
   Reduces internal support requests.
   Ensures up-to-date documentation access.

   
   

2. Self-Service Technical Helpdesk for Installers

   Installation contractors frequently ask repetitive technical questions that consume engineering team resources.

   

   Solution:
   A chatbot trained with RAG retrieves standards, product specs, and training documents to deliver accurate answers via web or mobile.

   Value:
   Improves partner experience.
   Frees technical staff from repetitive support.
   Scales knowledge delivery without added headcount.

   
   

Use Cases for Internal Knowledge & Regulatory Compliance

1. Organizational Knowledge Retrieval for Teams

   Project teams in engineering and construction firms often need quick access to internal policies, BIM guidelines, or archived specs.

   

   Solution:
   A RAG engine indexes internal files (contracts, standards, memos), enabling staff to query them conversationally for context-specific tasks.

   Value:
   Shortens onboarding time.
   Supports consistent internal workflows.
   Preserves institutional memory.

   
   

2. Regulation-Aware Design Review

   Design teams struggle to interpret evolving accessibility, fire safety, or zoning rules across jurisdictions.

   

   Solution:
   A RAG system trained on regional regulations answers queries like “Does this corridor meet local accessibility code?” by citing specific legal clauses.

   Value:
   Reduces legal risk and redesigns.
   Empowers designers with just-in-time compliance checks.
   Improves coordination with authorities and consultants.