Technology Videos

RAG or Fine-Tuning? Most People Get This Wrong...

The speaker warns that many organizations mistakenly favor fine‑tuning LLMs over Retrieval‑Augmented Generation (RAG), despite fine‑tuning’s high data, expertise, and cost requirements. Fine‑tuning demands millions of tokens, extensive data cleaning, and specialized ML talent to avoid over‑ or under‑training, making it time‑ and budget‑intensive. RAG, by contrast, externalizes knowledge, letting the model reference up‑to‑date external data without altering the model itself, simplifying maintenance and enabling source citation. While RAG is generally the preferred first step for domain‑specific applications, fine‑tuning may be added later for deeper expertise or response tailoring.