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KNOWLEDGE

Retrieval-Augmented Generation (RAG) Frameworks

Frameworks and patterns that combine document retrieval with language-model generation so AI answers are grounded in real source material.

  • Category Knowledge
  • Used In 1 Cortex Service

Technology Overview

What Is Retrieval-Augmented Generation (RAG) Frameworks?

Retrieval-augmented generation (RAG) is an architectural pattern that retrieves relevant passages from a knowledge base at query time and provides them to a language model as context, so the model's response is grounded in specific source documents rather than relying solely on what it learned during training.

At a Glance

Key Capabilities

  • RAG frameworks handle document chunking and preprocessing, embedding generation, retrieval-query construction, and prompt assembly that combines retrieved context with the user's question, along with mechanisms to cite or reference the source passages used.
How Cortex Uses It
Cortex Texas AI builds RAG pipelines as the foundation for enterprise knowledge assistants and grounded support tools, so answers are traceable back to a client's actual documents rather than being generated from general model knowledge alone, which materially reduces the risk of fabricated or outdated answers.
  • Accuracy
  • Latency
  • Cost
  • Privacy
  • Integration
  • Multimodal Needs
  • Reasoning Depth

Connectivity

Integration Scenarios

  • 01

    Common integration scenarios include internal knowledge assistants for employees, customer-facing support assistants grounded in documentation, and research or compliance tools that need answers traceable to specific source documents.

Model Selection

The Right Model Depends on the Workload

RAG is one of several approaches to grounding AI output in specific knowledge (fine-tuning being another); Cortex Texas AI selects the right approach, or combination, based on how frequently the underlying knowledge changes and how important source traceability is to the use case.

In Practice

Used In These Services

01

RAG & Enterprise Knowledge Systems

Retrieval-augmented AI systems that answer questions grounded in your own documents, with accurate, source-linked responses.

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Related Insights

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Sep 1, 2026

Vector Databases Explained

A vector database stores numeric representations of content and finds the closest matches by meaning, not exact keywords, making it the retrieval backbone behind most RAG and enterprise knowledge systems.

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Aug 25, 2026

RAG vs. Fine-Tuning: Choosing the Right Approach

RAG and fine-tuning are two different ways to make a general AI model useful for your specific data and needs. Most enterprise use cases are better served by RAG; fine-tuning solves a narrower, different problem.

Read article

Aug 18, 2026

What Is RAG (Retrieval-Augmented Generation)?

RAG (retrieval-augmented generation) grounds an AI model's answers in passages retrieved from your own documents at query time, rather than relying solely on what the model learned during training.

Read article

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