AB-731: When to Use Grounding vs. Fine-Tuning

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Should you use a packaged AI product, build with a pretrained model, add grounding, or consider fine-tuning? In this AB-731 lesson, we walk through a business-first approach to generative AI selection: choose the least customized solution that meets your business and control requirements. You’ll learn: • How solution selection differs from model strategy • When packaged products or custom applications make sense • How to establish a pretrained model baseline • When grounding is needed for current or private organizational information • When persistent behavior, style, or format gaps may justify fine-tuning • How safety, latency, total cost, and operational ownership affect the decision The key distinction is context versus learned behavior. Grounding supplies information at request time; fine-tuning changes model behavior through additional training. Neither replaces the need for appropriate access controls and output evaluation. For AB-731 preparation, practice identifying the actual gap in a scenario. Does the solution need better access to facts, more consistent behavior, or a different application workflow? Use that evidence to guide your choice. We finish with a practical five-step evaluation sequence, from testing product fit to assessing the complete solution. Continue learning with the AB-731 Microsoft AI Transformation Leader playlist on this channel. Subscribe to LearningPathX for clear explanations of certification concepts and practical decision-making. #AB731 #MicrosoftCertification #AITransformationLeader