Maximize AI With Clear Understanding of Fine-Tuning vs. Retrieval-Augmented Generation (RAG)

By Simon Harrison
Analyst and Client Executive Partner

Simon is an industry analyst who has authored over 30 Gartner Magic Quadrant notes as lead and with colleagues. He’s written important research as the Chief of Research advisor for Gartner. He continues to provide deep research and insights as an Executive Partner for Actionary clients and the industry.

Summary

Fine-Tuning and RAG (Retrieval-Augmented Generation) are essential AI model enhancement methods, each offering distinct advantages and challenges. Implementing these approaches strategically, based on specific AI application needs, can significantly enhance the performance and outcomes of pre-trained models. Conversely, choosing the wrong approach can undermine the benefits of an AI solution. This research explores the benefits of both Fine-Tuning and RAG, emphasizing the importance of selecting and combining these methods appropriately to achieve optimal results.

Key Take: Companies that fail to align Fine-Tuning and RAG with the appropriate use cases will encounter inefficiencies, diminished performance, and missed opportunities to fully leverage AI’s potential.

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