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    What is Retrieval Augmented Generation (RAG)?

    Retrieval Augmented Generation (RAG) is an AI technique that combines large language models with real-time access to external data. Instead of relying only on pre-trained knowledge, RAG retrieves relevant documents or facts during generation to improve accuracy and reduce hallucinations. For businesses, this means more reliable AI outputs for decision-making, reporting, and customer interactions.

    Expanded Definition

    Traditional language models generate text based only on patterns learned during training. The drawback? They can miss context, especially if the information has changed since the training data was collected. RAG solves this by inserting a retrieval step: when given a prompt, the system searches connected knowledge bases, APIs, or databases and feeds the findings back into the model. This creates outputs that are both fluent and grounded in up-to-date, verified content.

    According to Gartner, retrieval-augmented methods are becoming essential for enterprise AI adoption because they help mitigate risk and increase trust in generative systems.

    Alteryx supports RAG-style workflows by enabling teams to connect models directly to governed, analytics-ready datasets. That way, organizations can generate insights with the assurance that they’re rooted in trusted, auditable sources.

    How Retrieval Augmented Generation (RAG) is Applied in Business & Data

    RAG helps enterprises extend the value of generative AI without sacrificing governance or accuracy.

    • Finance teams can generate reports that reference the latest compliance documents.
    • Customer support can provide consistent answers by grounding responses in knowledge bases.
    • Supply chain leaders can run “what-if” analyses using live logistics data rather than static assumptions.

    In each case, RAG improves outcomes by ensuring that the “G” (generation) stage is informed by real-world, current, and contextualized data.

    How Retrieval Augmented Generation (RAG) Works

    RAG combines search and generation to make AI answers more reliable:

    1. Finds the right info – Scans internal or external sources like reports, documents, or databases.
      • Breaks documents into smaller chunks (often 200–500 tokens) so the system can pull precise context.
      • Translates queries and documents into numbers so it can quickly match and surface the closest, most relevant passages.
    2. Adds context & generates answers – The retrieved information is given to the language model as supporting evidence.
      • The model then produces a grounded response.
      • Since models have limited memory (“context windows”), RAG systems rank and trim results so only the best information is used.

    At scale, RAG systems use vector databases like Pinecone, Weaviate, or Elastic to speed up similarity search. This helps ensure outputs stay factual and explainable, which is key for enterprise adoption.

    Use Cases

    • Generating market research summaries using internal reports and third-party datasets.
    • Automating regulatory filings with references to the latest compliance rules.
    • Powering chatbots that provide precise, context-based answers.

    Industry Examples

    • Banking: Automating KYC (Know Your Customer) checks by retrieving the latest policy and customer history.
    • Healthcare: Summarizing patient histories while referencing current clinical guidelines.
    • Retail: Enriching personalized recommendations with both purchase history and live inventory data.

    FAQs

    Does RAG replace data governance?
    No. RAG relies on curated and governed data sources to be effective. Without strong governance, retrieval risks introducing bias or errors.

    Is RAG the same as fine-tuning?
    No. Fine-tuning permanently updates a model’s parameters. RAG, by contrast, adds context dynamically at runtime, which makes it more flexible and less resource-intensive.

    How do you measure RAG quality?

    RAG quality is measured across several dimensions:

    • Retrieval precision – Are the right documents or passages being pulled?
    • Generation faithfulness – Are answers clearly grounded in those documents?
    • Relevance – Does the response directly address the user’s query?
    • User satisfaction – Are people finding the answers useful and trustworthy?
    • Latency – Can the system deliver results quickly enough for real-world use?

    Together, these metrics help organizations balance accuracy, usability, and performance when evaluating RAG systems.

    Synonyms

    • Retrieval-based Generation
    • Augmented LLMs
    • Knowledge-grounded Generation

    Related Terms

     

    Last Reviewed:

    September 2025

    Alteryx Editorial Standards and Review

    This glossary entry was created and reviewed by the Alteryx content team for clarity, accuracy, and alignment with our expertise in data analytics automation.