Retrieval-augmented generation

As explained in Authorization in Action

Retrieval-augmented generation is a technique that supplies a generative model with externally retrieved information when producing an output.

How Authorization in Action covers it

How deep it goes

Explains RAG’s data flow and safety boundary, emphasizing authorization before retrieval rather than detailed implementation.

Examples

Uses a financial-records assistant and enterprise document or record retrieval to illustrate authorization-sensitive responses.

What you can do after

Operate/configure RAG systems by enforcing authorization before retrieval and sending only permitted data to the model.

How it connects

Connects large language models, enterprise data, authorization, policy engines, access control, and AI accountability.

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Also explained in

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  Grokking AI ApplicationsA generative AI technique that retrieves relevant external information and supplies it to a model as context for generation.
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  Effective Conversational AIRetrieval-augmented generation is a technique that conditions generated responses on information retrieved from external documents or data sources.
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  Domain-Specific Small Language ModelsAn application system that combines retrieval from external sources with text generation to ground outputs in outside knowledge.
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  Software Testing with Generative AIRetrieval-augmented generation is a generation technique that retrieves relevant external information and supplies it to a language model as context.
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  LLMs in ProductionRetrieval-augmented generation is a technique that supplies a generative model with information retrieved from external sources at response time.
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  AI-Powered SearchRetrieval augmented generation is a technique that supplies retrieved external information to a generative model as context for producing a response.
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  Natural Language Processing in Action, Second EditionRetrieval-augmented generation is a language-generation technique that retrieves external information and conditions generation on the retrieved information.
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  Machine Learning Platform EngineeringRetrieval-augmented generation is a method that supplies retrieved external information to a generative model when producing an output.
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  Building Reliable AI SystemsA retrieval-augmented generation approach that retrieves and synthesizes stored information without taking actions in the real world.
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  Build AI-Enhanced Web AppsRetrieval-augmented grounding is a grounding technique that retrieves relevant external information and incorporates it into response generation.
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  The Art of AI Product DevelopmentAn architecture that retrieves relevant external information and supplies it to a generative model as context for producing an output.
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  Generative AI in ActionRetrieval-augmented generation is a generation technique that retrieves relevant external information and supplies it to a generative model as context for its response.
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  Financial AI in PracticeA method that retrieves relevant external information and supplies it to a generative model when producing an output.
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  Introduction to Generative AI, Second EditionRetrieval-augmented generation is a generative technique that retrieves external information and supplies it to a model as context before generation.
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  AI Agents and ApplicationsRetrieval-augmented generation is a technique that retrieves relevant external information and supplies it to a language model as context for response generation.
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  Data Pipelines with Apache Airflow, Second EditionA RAG workflow is a retrieval-augmented generation workflow that retrieves relevant information and supplies it to a generative model for producing an output.
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  Investing for ProgrammersRetrieval-augmented generation is a technique that retrieves relevant external information and supplies it to a generative model when producing a response.
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  Knowledge Graphs and LLMs in ActionRetrieval-augmented generation is a technique that supplies a generative language model with relevant information retrieved from an external source before answer generation.
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  Build Python Web Apps with StreamlitRetrieval-augmented generation is a technique that retrieves relevant external information and supplies it to a language model when generating a response.
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  Data Storytelling with Altair and AIRetrieval-augmented generation is a technique that supplies a language model with relevant information retrieved from an external knowledge source when generating output.
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  AI Agents in Action, Second EditionRetrieval-augmented generation is a technique that retrieves pertinent information at generation or decision time and adds it to a model's context.
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  Spring AI in ActionRetrieval-augmented generation is a technique that supplies retrieved external information to a generative model when producing a response.
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  Just Use Postgres!Retrieval-augmented generation is a technique in which a generative model incorporates information retrieved from an external knowledge source when generating an answer.
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  Transformers in ActionA retrieval-augmented generation system generates outputs using information retrieved from an external collection.
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  AI Agents in ActionRetrieval augmented generation is a language-model pattern that retrieves relevant external information and incorporates it into generated responses.
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  MongoDB 8.0 in Action, Third EditionRetrieval-augmented generation is an AI technique that retrieves external information and supplies it to a generative model when producing a response.
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  Essential GraphRAGA retrieval-augmented generation pipeline combines information retrieval with language-model generation to produce responses grounded in retrieved information.
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  Machine Learning System DesignA generation approach that retrieves relevant external information and supplies it to a generative model as input.
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  Build Applications with Local AI Models on a MacRetrieval-augmented generation is a technique that retrieves relevant information from an external knowledge source and supplies it to a language model alongside a question or instruction.
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  The AI Pocket BookA method of augmenting an LLM prompt with retrieved external documents so the model can answer using information not in its training data.
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  Building LLM Applications with DSPyA language-model application pattern that retrieves external information and supplies it as context for response generation.

Compare how 32 books explain retrieval-augmented generation →

Related concepts

• language modeluses · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• similarity searchuses · Similarity search is a retrieval technique that finds or ranks items by comparing their representations with a query according to a similarity or distance measure.
• hallucinationaddresses · A hallucination is model-generated content that is false, fabricated, or unsupported by relevant evidence but presented as plausible or factual.
• knowledge baseuses · A knowledge base is an organized collection of information that can be accessed or queried to support reference, retrieval, or reasoning.
• information retrievaluses · Information retrieval is the process of locating and selecting relevant information from an available collection in response to a query or information need.
• fine-tuningcontrasts with · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.

This explainer was generated by Manning's knowledge graph from the text of Authorization in Action. The section links point to the parts of the book it draws on.