RAG vs Fine-Tuning: Which One Should You Actually Use
A field-tested comparison of retrieval-augmented generation and parameter-efficient fine-tuning — scored on cost, latency, freshness, and failure modes, with a decision rule you can apply today. A logistics company in Dubai called me in to fix a support bot that kept hallucinating their shipping policy. Their previous consultant had spent three weeks fine-tuning a 7B model on their internal PDFs,…
Retrieval-Augmented Generation (RAG) and parameter-efficient fine-tuning are two techniques used to enhance language models. While they share some similarities, they are not interchangeable and have distinct advantages and disadvantages depending on the use case. Recently, a logistics company in Dubai sought the help of a reporter to fix a support bot that kept quoting outdated shipping policies.
The bot, previously fine-tuned on 7B model parameters, cited policies from January instead of the current March version. After investigating, the reporter found that the issue stemmed from a lack of understanding of the differences between RAG and fine-tuning, leading to wasted resources and potential trust issues.
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