I Spent 3 Hours Building Something That Would Have Taken My Team 3 Weeks. It’s Called RAG.

2026-05-16 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

Large Language Models (LLMs) are fundamentally limited to the data they were trained on, leading to "hallucinations" when queried about specific, proprietary information not included in their training corpus. This limitation means LLMs cannot directly answer questions about internal company documentation, recent product releases, or updated policy documents. The article highlights that attempting to use a raw LLM for such tasks is akin to using the wrong tool for the job. Instead, it introduces Retrieval Augmented Generation (RAG) as the necessary solution to enable LLMs to provide accurate, context-specific answers by supplying them with relevant external information at inference time, thereby overcoming their inherent knowledge boundaries.

Key takeaway

For AI Engineers building internal knowledge systems, understanding the inherent limitations of raw LLMs is critical. Your team should implement Retrieval Augmented Generation (RAG) to prevent hallucinations and ensure accurate responses when querying LLMs against proprietary or recently updated data. This approach significantly enhances the utility of LLMs for enterprise applications, transforming them from general knowledge tools into reliable, context-aware assistants.

Key insights

LLMs are limited to their training data, necessitating RAG for accurate, context-specific responses to novel queries.

Principles

• LLMs only know what they were trained on.
• Raw LLMs hallucinate when lacking specific context.

In practice

• Use RAG for internal documentation queries.
• Avoid raw LLMs for proprietary data tasks.

Topics

• Retrieval-Augmented Generation
• Large Language Models
• LLM Hallucinations
• Knowledge Bases
• Contextual AI

Best for: AI Engineer, Machine Learning Engineer, Data Scientist

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