Build a RAG system from scratch — Part 1

2026-06-14 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Novice, quick

Summary

The article introduces Retrieval-Augmented Generation (RAG) systems, explaining their two core components: retrieval and augmented generation. The author, participating in the LLM ZoomCamp, aims to systematize their understanding of RAG. RAG systems begin with a user query, which triggers the "Retrieval" component to pull relevant data from a knowledge base. This process involves indexing the data for efficient keyword or semantic search, reducing LLM hallucinations by anchoring responses to specific information. The "Augmented Generation" component then uses this retrieved data, along with a pre-built prompt, to guide an LLM in formulating a polished answer to the user's question. This first part of a series provides a foundational overview, with subsequent parts promising detailed explanations of each component.

Key takeaway

For AI Engineers building LLM applications, understanding the foundational two-component structure of Retrieval-Augmented Generation (RAG) is crucial. This architecture directly addresses LLM hallucination by anchoring responses to specific, retrieved data. You should prioritize robust data indexing and prompt engineering to effectively guide LLMs, ensuring accurate and contextually relevant outputs from your systems.

Key insights

RAG systems combine retrieval from a knowledge base with LLM-driven augmented generation to provide relevant, hallucination-reduced answers.

Principles

• RAG reduces LLM hallucination by anchoring to a knowledge base.
• Effective retrieval requires data indexing for fast search.
• Prompts guide LLMs to use only retrieved data.

Method

A RAG system processes a user query by first retrieving relevant data from an indexed knowledge base, then feeding this data and a guiding prompt to an LLM for augmented generation.

Topics

• Retrieval-Augmented Generation
• LLM Applications
• Knowledge Bases
• Data Indexing
• Semantic Search
• Prompt Engineering

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

Related on AIssential

• Retrieval Augmented Generation (RAG) system — RAG systems enhance LLMs by integrating external knowledge retrieval, mitigating hallucination and outdated information.
• I Spent 3 Hours Building Something That Would Have Taken My Team 3 Weeks. It’s Called RAG. — LLMs are limited to their training data, necessitating RAG for accurate, context-specific responses to novel queries.
• Büyük Dil Modellerinin Hafızasını Güçlendiren Yaklaşım:Retrieval-Augmented Generation (RAG) — RAG enhances LLMs by providing external, real-time context, reducing hallucinations and improving factual accuracy.
• The Complete Guide to RAG: Why Retrieval-Augmented Generation Is the Backbone of Enterprise AI in… — RAG enables LLMs to access real-time external data, overcoming knowledge cutoffs and reducing hallucinations.
• RAG Explained Simply: The Brain Behind Modern AI Chatbots — RAG combines LLMs with external knowledge retrieval to produce contextually relevant and factually grounded AI responses.
• How RAG Changed AI Forever — RAG addresses LLM hallucination and fine-tuning costs by dynamically providing real-time, external context for grounded generation.

Open in AIssential →

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