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As someone with technical expertise in data science & ML, who also experimented with earlier generative models (e.g., GANs), I was quite excited and a bit surprised about how well LLM applications were performing. However, I had concerns about using GenAI, especially LLMs, reliably in real-life business applications. The volatility and limited explainability of these models, caused by their complexity and massive training data, worried me especially as I guide clients as a Senior AI Strategist at a German consultancy.

Which course did you take?

The “Generative AI with Large Language Models” course chile cell phone number list by DeepLearning.AI not only solidified my knowledge but also addressed my concerns while highlighting budget-friendly ways teams can apply GenAI.

How are you thinking about GenAI now?

Surprisingly, the course gave me more hope regarding the democratization of GenAI. It demonstrated how small, open-source models could perform very well in certain use cases with adequate fine-tuning and alignment techniques. This approach gives developers and companies more control over their models, making GenAI accessible with a reasonable budget.

Are you still concerned?