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Prompt engineering is more than a productivity trick. Satvik Tripathi explains why prompting matters for reliability, safety and responsible use of AI in medical imaging. Jason Nagels asks Satvik Tripathi why prompting matters beyond saving time. Satvik compares prompting to hyperparameter search: a scientific method for understanding where a model performs best. Because LLMs predict the next token, adding context narrows what the model retrieves and increases the probability of a correct answer, which he illustrates with an apple example that narrows to a green apple in Kentucky. He likens it to a game of hangman, where clues guide the prediction. Satvik also reminds viewers that these are powerful NLP models, and that their zero-shot performance can be comparable to BERT models trained on local datasets, without the painful training process. Want to go deeper on imaging informatics, enterprise imaging and healthcare AI? Check out Nagels Consulting at https://www.nagelsconsulting.com for consulting, training and certification prep (including CIIP). If you found this clip useful, give it a like, subscribe for more conversations with healthcare and medical imaging leaders, and tell us in the comments: what prompting techniques have improved your AI results? #PromptEngineering #HealthcareAI #MedicalImaging #LLM #GenerativeAI #ResponsibleAI #NLP #Radiology #ImagingInformatics #NagelsConsulting TAGS: prompt engineering, prompt engineering healthcare, AI in medical imaging, reliable AI, responsible AI healthcare, AI safety, large language models, LLM prompting, NLP healthcare, zero-shot learning, BERT, hyperparameter tuning, generative AI healthcare, radiology AI, imaging informatics, healthcare AI, Satvik Tripathi, Nagels Consulting

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