Blog: Learn Machine Learning from a Google AI Engineer
Beyond the Basics How to Choose and Configure Your LLM for Maximum Impact
When it comes to AI prompt engineering, the choice of language model (LLM) is crucial. Each prompt should be carefully optimized for the specific LLM you’re using, whether it’s one of the Gemini language models in Vertex AI, GPT, Claude, or an open-source model like LLaMA. It’s important to note that a prompt that was optimized for TextBison won’t necessarily work in Gemini. This principle holds true not just across different models but also between different versions of the same model.
This is the 3rd blog of the series: Prompt engineering for business applications. Prompt Engineering is complex and requires careful planning and refinement to achieve desired results from AI models. As a software engineer @Google with experience in prompt engineering for major businesses, I will share practical learnings in a blog series to help others unlock the power of AI beyond simple tasks.
When it comes to AI prompt engineering, the choice of language model (LLM) is crucial. Each prompt should be carefully optimized for the specific LLM you’re using, whether it’s one of the Gemini language models in Vertex AI, GPT, Claude, or an open-source model like LLaMA. It’s important to note that a prompt that was optimized for TextBison won’t necessarily work in Gemini. This principle holds true not just across different models but also between different versions of the same model.
This is the 3rd blog of the series: Prompt engineering for business applications. Prompt Engineering is complex and requires careful planning and refinement to achieve desired results from AI models. As a software engineer @Google with experience in prompt engineering for major businesses, I will share practical learnings in a blog series to help others unlock the power of AI beyond simple tasks.
Lee Boonstra
Lee Boonstra (they/them) has been a presence in the tech world since 2007, wearing many hats from AI software engineer to prompt engineer, web developer to technical trainer, and developer advocate.
With eight years of experience at Google (and AI) under their belt, they now hold the role of AI Software Engineer (SWE) at the Google Cloud office of the CTO. Leading innovation projects, Lee aims to disrupt markets and foster collaboration globally. Their expertise in Conversational and Voice technology, alongside (Generative) AI, has led to recognition as a respected public keynote speaker and published author for O’Reilly and Apress. Lee eases tech headaches and celebrates those light bulb moments.
Lee has written the Google/Kaggle Prompt Engineering whitepaper and two books: "Hands-on Sencha Touch 2" (O'Reilly) and their more recent work, "The Definitive Guide to Conversational AI with Dialogflow and Google Cloud" (Apress).
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