{
  "id": 8419895,
  "title": "How LLMs Actually Work: A Practical Guide for Product Managers",
  "url": "https://urgent.news/2026/09/19/how-llms-actually-work-a-practical-guide-for-product-managers",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-19T08:36:46.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/abhishekjaiswal_4896/how-llms-actually-work-a-practical-guide-for-product-managers-3k7b"
  },
  "original_language": "en",
  "account": "For Product Managers developing AI products, understanding Large Language Models (LLMs) is crucial. However, you don't need to be an ML researcher. Instead, focus on grasping the core concepts. At its simplest, an LLM is a machine learning model trained on vast datasets to recognize language patterns and generate outputs based on input. A practical way to conceptualize an LLM is as follows: \"An LLM takes tokens as input and predicts what token should follow next.\"\n\nThe process begins with tokenization. When you input text, the model doesn't receive it directly. Rather, the text is transformed into tokens, which aren't always complete words. For instance, the phrase \"internationalization\" may be broken down into smaller tokens. This distinction matters because tokens influence things like API costs, context window usage, latency, and throughput.\n\nFollowing tokenization, the tokens are converted into numerical representations known as embeddings. Neural networks process numbers, not text. Embeddings act as numerical representations that enable the model to understand relationships between information pieces.\n\nThe truly transformative part, however, is the Transformer architecture, introduced in the 2017 paper \"Attention Is All You Need.\" Transformers rely on attention mechanisms instead of recurrent models. A simplified Transformer block consists of self-attention and feed-forward networks. Self-attention allows the model to understand relationships between tokens, with the concept of Queries, Keys, and Values facilitating this process.\n\nUnderstanding these fundamental aspects of LLMs equips Product Managers to make informed decisions regarding AI product development, such as choosing between using pre-existing LLM APIs or building custom models, tackling performance issues, or deciding on techniques like Retrieval Augmented Generation (RAG) or fine-tuning.",
  "summary": "If you're a Product Manager working on AI products, you don't need to become an ML researcher. But you do need to understand what happens inside an LLM. Because sooner or later, you'll have to answer questions like: Should we use an existing LLM API or build our own model? Why is our AI feature slow? Why does the model hallucinate? Do we need RAG or fine-tuning? Why did our token usage suddenly…",
  "key_points": [
    "LLMs are machine learning models trained on vast datasets to recognize language patterns",
    "Tokenization transforms input text into tokens, influencing API costs and context window usage",
    "Transformer architecture, introduced in 2017, uses attention mechanisms for token relationships"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}