Author(s): Laura Verghote

Originally published on Towards AI.

A practical framework to choose between RAG, fine-tuning, continued pre-training, and training from scratch — through the lens of balancing cost, control, performance and compliance.

As Generative AI systems move from prototypes to production, the real challenge isn’t model accuracy; it’s balancing compute cost, customization depth, and operational complexity. Between training cycles and data pipelines, many teams discover too late that their large language model (LLM) strategy is as much a financial and architectural decision as it is a technical one.

This article discusses various strategies for customizing large language models (LLMs), emphasizing the importance of selecting the right approach based on cost, control, and performance. It highlights techniques such as prompt engineering, retrieval-augmented generation (RAG), fine-tuning, continued pre-training, and training from scratch. The article elaborates on when to opt for custom models versus utilizing existing frameworks, the total cost of ownership, and the implications for operational effectiveness. The conclusion stresses the balance between initial costs and long-term investments in customization strategies.

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Published via Towards AI