There are 4 core concepts you need to understand as a Product Manager working with AI: LLMs — Large Language Models like GPT or Claude. These are the engines. They take in a prompt — some text — and try to predict the next best word or sentence. They can write emails, summarize docs, generate code — all by learning from patterns in huge amounts of data. Embeddings — Think of these as meaning maps. An embedding turns a sentence into a list of numbers that capture its meaning. This lets us compare ideas mathematically — like, ‘how similar is this user query to a help article?’ That’s how search, recommendations, and RAG work. Transformers — This is the name of the architecture that powers modern LLMs. It’s how models like GPT understand relationships between words in a sentence. We’ll go deeper into this, but for now: it’s the reason models today are so much smarter than the ones from 5 years ago. Tokens — A token is like a building block of language for these models. It might be a word, part of a word, or even a punctuation mark. Why does it matter? Because every API call and model response is priced and limited based on tokens, not words. Each of these will get its own slide and example — but for now, just remember: this is your AI survival kit.

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Welcome! As a Product Manager working with AI, there are four core concepts you absolutely need to understand. These form your AI survival kit: Large Language Models or LLMs, Embeddings which are like meaning maps, Transformers the architecture powering modern AI, and Tokens the building blocks of language. Let's explore each of these essential concepts. Large Language Models, or LLMs, are the engines that power modern AI applications. Think of them as sophisticated prediction machines. When you give an LLM a prompt, it analyzes the text and predicts what should come next, word by word. This simple concept enables LLMs to write emails, summarize documents, generate code, and answer complex questions. They achieve this by learning patterns from massive datasets containing billions of words from books, articles, and web content. Embeddings are like meaning maps for AI systems. They convert text into numerical vectors that capture semantic meaning. When you have two similar sentences like 'Dog runs fast' and 'Cat moves quickly', their embeddings will be mathematically close to each other. This mathematical representation enables AI to compare ideas, power search systems, drive recommendation engines, and make RAG systems work by finding relevant information based on meaning rather than just keyword matching. Transformers are the revolutionary architecture that powers modern large language models like GPT and Claude. The key innovation is the attention mechanism, which allows the model to understand relationships between all words in a sentence simultaneously. Unlike older models that processed words sequentially, transformers can process everything in parallel, making them much faster and more effective at understanding context. This breakthrough is why today's AI models are dramatically smarter than those from just five years ago. Finally, let's talk about tokens - the building blocks of language for AI models. A token might be a complete word like 'AI', part of a word like 'under' and 'standing', or even punctuation marks. For Product Managers, understanding tokens is crucial because every API call and model response is priced and limited based on tokens, not words. This means longer conversations cost more money, and there are hard limits on how much text you can process at once. By understanding tokens, you can optimize your prompts and control costs effectively. These four concepts - LLMs, Embeddings, Transformers, and Tokens - form your essential AI survival kit as a Product Manager.