Organizations should prioritize retrieval-augmented generation and prompt engineering over continuous model training due to cost-effectiveness and sustainability. The rapidly evolving generative AI landscape makes ongoing fine-tuning impractical, resulting in financial strains from constant retraining. Instead, enhancing retrieval methods and optimizing prompts provides a more adaptable approach, allowing for the integration of new technology without continuous investment in model retraining or fine-tuning, making it a strategic choice for initial AI adoption.
The rapid development in generative AI technology makes constant fine-tuning costly, emphasizing retrieval-augmented generation and prompt engineering as a more sustainable adoption strategy.
Training and fine-tuning language models requires significant resources and can lead organizations into an expensive cycle of keeping up with new technology.
Relying on retrieval-augmented generation and prompt engineering allows organizations to continuously leverage advancements in generative AI without the extensive cost of model retraining.
As new robust models emerge, waiting and refining retrieval methods proves more efficient than investing heavily in specialized model training.
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