How RAG Works: A Technical Overview
Retrieval Augmented Generation (RAG) solves the core limitation of large language models (LLMs): they are trained on public data and have no knowledge of your organisation's private documents, policies, or real-time information. RAG bridges this gap by retrieving relevant passages from your own knowledge base and injecting them into the LLM prompt at query time — giving your AI assistant accurate, grounded answers based on authoritative internal sources.
Contellect's RAG-as-a-Service pipeline consists of four stages: ingestion (chunking and vectorising your documents), indexing (storing vectors in a managed Azure AI Search or Qdrant index), retrieval (semantic similarity search at query time), and generation (LLM prompt augmentation with retrieved context).
Why Choose RAG-as-a-Service vs. Build Your Own?
| Factor | DIY RAG | Contellect RAG-as-a-Service |
|---|
| Time to production | 3–9 months | 2–6 weeks |
| Engineering cost | $200K–$500K | SaaS subscription |
| Ongoing maintenance | Dedicated ML team | Managed by Contellect |
| Document security | Varies | Azure-native, data stays in your tenant |
| LLM flexibility | Single model | GPT-4o, Claude, Mistral, custom |
Enterprise RAG Use Cases
Knowledge Base Q&A
Give employees instant answers from policy manuals, HR handbooks, and technical documentation — without needing to search SharePoint or Confluence. Response accuracy exceeds keyword search by 40–60% on long-tail queries.
Intelligent Contract Review
Upload a contract library and ask natural-language questions: "Which contracts expire in Q3?", "Which agreements lack a governing law clause?". Contellect's RAG returns cited answers with source document links.
Regulatory Compliance Assistant
Index regulatory documents (GDPR, Basel III, HIPAA) alongside your internal controls and ask compliance questions. Get instant gap analysis with specific paragraph citations.
Customer-Facing Chatbot
Deploy a product knowledge chatbot grounded in your documentation, FAQs, and case studies. Unlike generic LLM chatbots, Contellect RAG will not hallucinate — answers are always grounded in your approved content.
Contellect's RAG Architecture
Contellect RAG-as-a-Service is built natively on Microsoft Azure, deployed within your own Azure tenant to ensure data sovereignty. The stack includes: Azure OpenAI (GPT-4o), Azure AI Search (vector + hybrid retrieval), Azure Document Intelligence (layout-aware chunking), and Contellect's orchestration layer for multi-step reasoning and source citation.
Frequently Asked Questions
- What is Retrieval Augmented Generation (RAG)?
- RAG is an AI architecture that enhances large language models by retrieving relevant passages from a document knowledge base at query time and injecting them into the prompt — ensuring the model's answers are grounded in authoritative, up-to-date sources rather than its training data alone.
- What is RAG-as-a-Service?
- RAG-as-a-Service means the full RAG pipeline (ingestion, vectorisation, indexing, retrieval, LLM integration, and UI) is hosted, managed, and maintained by a vendor — allowing enterprises to deploy production-grade AI Q&A on their own documents within weeks, without building or maintaining the infrastructure themselves.
- How does Contellect's RAG handle private enterprise data?
- Contellect RAG is deployed entirely within your Microsoft Azure tenant. Your documents never leave your environment — all vector embeddings and LLM calls are processed using your own Azure OpenAI resource. Contellect has zero access to your document content.
- What LLMs does Contellect's RAG support?
- Out of the box, Contellect supports Azure OpenAI (GPT-4o, GPT-4 Turbo), Anthropic Claude (via Azure), and open-source models (Mistral, Llama 3) deployed on Azure ML managed compute. Custom model integrations are available on the Enterprise plan.
