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2 clusters · 3 sources · 6 days · First seen · Last updated

Retrieval-Augmented Generation technology overview

Overview

Retrieval-Augmented Generation (RAG) is an AI technique used to ground large language models (LLMs) in external, authoritative, or domain-specific data. This method aims to reduce AI hallucinations and provide up-to-date information without the necessity of expensive retraining or fine-tuning.

The technical workflow generally involves a multi-step pipeline: documents are ingested, broken into smaller chunks, converted into vector embeddings, and stored in vector databases. When a user submits a query, the system retrieves the most semantically relevant sections to provide context for the LLM to synthesize a response.

Implementation details include the use of a retriever to source information, a ranker to prioritize results, and orchestration tools such as n8n. While semantic caching can be used to improve latency and cost by serving previous answers, it does not create a persistent model of domain understanding; if a query falls outside similarity thresholds, the reasoning process must restart.

Entities

Azure AI Search · OpenAI · Pinecone · n8n

Timeline

  1. 23 days ago

    [TECHNOLOGY] 2 sources
    Retrieval-Augmented Generation (RAG) explained

    Retrieval-Augmented Generation (RAG) improves AI accuracy by connecting large language models to external data sources, reducing hallucinations and providing up-to-date, domain-specific context.

  2. 29 days ago

    [TECHNOLOGY] 2 sources
    RAG Frameworks: Understanding Retrieval-Augmented Generation

    An overview of Retrieval-Augmented Generation (RAG) frameworks, detailing the pipeline of ingestion, retrieval, and generation, and the limitations of semantic caching in building persistent knowledge.

Sources

brandaktuell.at · dev.to · towardsdatascience.com