Retrieval Augmented Generation (RAG) system

2026-03-26 · Source: Deep Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

Retrieval Augmented Generation (RAG) systems combine a retriever and a generator to overcome the limitations of vanilla Large Language Models (LLMs), such as hallucination and outdated knowledge. Unlike traditional LLMs that store knowledge in static parameters, RAG systems outsource information retrieval to external memory, fetching relevant documents at inference time. The workflow involves a user query being mapped into an embedding space by a retriever, which then retrieves documents based on similarity scores. These documents condition a generator (LLM) to produce the final answer. Two main variants exist: RAG-Sequence, which uses the same documents for the entire output, and RAG-Token, which retrieves dynamically per token. While RAG enhances LLM capabilities, its effectiveness is contingent on the quality of the source data, the accuracy of the retrieval process, and the generator's ability to correctly interpret and utilize the retrieved context.

Key takeaway

For AI Architects designing LLM applications, RAG offers a robust solution for addressing knowledge freshness and hallucination. You should prioritize rigorous evaluation of your data sources, optimize retrieval mechanisms through careful embedding and chunking, and fine-tune generator behavior to ensure accurate context interpretation. This multi-stage quality control is crucial for deploying reliable and performant RAG systems in production environments.

Key insights

RAG systems enhance LLMs by integrating external knowledge retrieval, mitigating hallucination and outdated information.

Principles

• RAG systems combine a retriever and a generator.
• Knowledge is outsourced to external memory for retrieval.
• Retrieval quality directly impacts generator output.

Method

A user query is embedded, relevant documents are retrieved via similarity, and these documents condition an LLM to generate an answer.

In practice

• Use RAG to provide LLMs with up-to-date information.
• Implement RAG-Token for dynamic, per-token retrieval.
• Focus on source, retrieval, and generator quality.

Topics

• Retrieval-Augmented Generation
• Large Language Models
• Information Retrieval
• Knowledge Augmentation
• Model Hallucination

Best for: AI Architect, AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Deep Learning on Medium.