A generation approach that retrieves relevant external information and supplies it to a generative model as input.
Retrieval-augmented generation separates information access from text generation: relevant data is pulled from a vector database and supplied to a large language model as prompt input. section 3.3
Use the pattern when:
A domain-specific language-model application needs information from an external data source. section 3.3
The system should decompose the task into retrieval and generation, then process the fused data downstream. section 3.3
How Machine Learning System Design covers it
How deep it goes
Defines the retrieval-and-prompting pattern but does not detail retrieval quality, indexing, prompting, or production operations.
Examples
Uses domain-specific LLM solutions that pull relevant data from a vector database before generation.
What it assumes
Assumes a generative model and an accessible external data source, specifically a vector database in the described pattern.
What you can do after
Apply it by retrieving relevant external data and inserting that data into a generative model’s prompt.
How it connects
Connects large language models, vector databases, data fusion, prompts, and problem decomposition.
Effective Conversational AIRetrieval-augmented generation is a technique that conditions generated responses on information retrieved from external documents or data sources.
Domain-Specific Small Language ModelsAn application system that combines retrieval from external sources with text generation to ground outputs in outside knowledge.
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.
LLMs in ProductionRetrieval-augmented generation is a technique that supplies a generative model with information retrieved from external sources at response time.
AI-Powered SearchRetrieval augmented generation is a technique that supplies retrieved external information to a generative model as context for producing a response.
Machine Learning Platform EngineeringRetrieval-augmented generation is a method that supplies retrieved external information to a generative model when producing an output.
Building Reliable AI SystemsA retrieval-augmented generation approach that retrieves and synthesizes stored information without taking actions in the real world.
Build AI-Enhanced Web AppsRetrieval-augmented grounding is a grounding technique that retrieves relevant external information and incorporates it into response generation.
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.
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.
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.
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.
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.
Investing for ProgrammersRetrieval-augmented generation is a technique that retrieves relevant external information and supplies it to a generative model when producing a response.
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.
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.
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.
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.
Spring AI in ActionRetrieval-augmented generation is a technique that supplies retrieved external information to a generative model when producing a response.
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.
AI Agents in ActionRetrieval augmented generation is a language-model pattern that retrieves relevant external information and incorporates it into generated responses.
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.
Essential GraphRAGA retrieval-augmented generation pipeline combines information retrieval with language-model generation to produce responses grounded in retrieved information.
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.
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.
Building LLM Applications with DSPyA language-model application pattern that retrieves external information and supplies it as context for response generation.
Authorization in ActionRetrieval-augmented generation is a technique that supplies a generative model with externally retrieved information when producing an output.
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 Machine Learning System Design. The section links point to the parts of the book it draws on.
