Blog: Learn Machine Learning from a Google AI Engineer
The Foundation Understanding LLMs and Prompt Engineering, and Why It All Matters
Let’s get down to basics and talk about how Large Language Models (LLMs) actually work. Think of them like prediction machines. There’s nothing factual; everything is statistical. It generates text, one word after another (well, technically it’s not a word, but it’s a token; multiple tokens may form a word), and then tries to guess what the next word should be. They’re trained on massive amounts of data, so they get pretty good at figuring out how words relate to each other.
This is the 2nd 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.
Let’s get down to basics and talk about how Large Language Models (LLMs) actually work. Think of them like prediction machines. There’s nothing factual; everything is statistical. It generates text, one word after another (well, technically it’s not a word, but it’s a token; multiple tokens may form a word), and then tries to guess what the next word should be. They’re trained on massive amounts of data, so they get pretty good at figuring out how words relate to each other.
This is the 2nd 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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