{
  "id": 1259699,
  "title": "From Neural Networks to LLMs: The Mental Model I Was Missing",
  "url": "https://urgent.news/2026/08/16/from-neural-networks-to-llms-the-mental-model-i-was-missing",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-16T11:30:08.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/priyankaa/from-neural-networks-to-llms-a-developers-mental-model-55a6"
  },
  "original_language": "en",
  "account": "In the article \"From Neural Networks to LLMs: The Mental Model I Was Missing,\" the author describes their journey to understand the inner workings of large language models (LLMs). Initially, they were overwhelmed by technical terms like neural networks, deep learning, Transformers, attention, tokens, embeddings, BERT, and GPT. To build a clear mental model, they decided to start from the beginning and progress step by step.\n\nThe article begins with an introduction to neural networks, explaining that they are machine learning models that learn patterns from data. For instance, in the case of recognizing handwritten digits, a neural network could be trained on thousands of images of handwritten digits, learning patterns without being explicitly programmed with rules. The patterns learned by the network are stored in its parameters, primarily weights and biases. As the number of layers in a neural network increases, it transitions into deep learning. CNNs are popular for image tasks, while RNNs and LSTMs are used for sequential data like language. Transformers become crucial for language tasks and many other sequence-based problems.\n\nThe author then delves into why Transformers emerged as the go-to architecture for language tasks. Before Transformers, RNNs and LSTMs were commonly used, but they had challenges such as inefficient parallel processing and difficulty in capturing long-range relationships between tokens. The Transformer architecture, introduced in the paper \"Attention Is All You Need\" in 2017, addressed these issues by using attention mechanisms to determine relationships between tokens, improving context understanding and allowing parallel computation during training.\n\nThe article then explains the concept of attention, which allows the model to focus on relevant tokens when processing a specific token. Self-attention is the mechanism where tokens in a sequence attend to other tokens within the same sequence. This high-level overview sets the stage for understanding the original Transformer architecture, which consists of two main components: the encoder and the decoder. The encoder processes the input and creates contextual representations, while the decoder generates the output sequence.\n\nThe author also discusses different Transformer architectures, such as encoder-only models (e.g., BERT) and decoder-only models (e.g., GPT). Encoder-only models, like BERT, process the input and create contextual representations, making them suitable for tasks like text classification, sentiment analysis, and named entity recognition. Decoder-only models, on the other hand, generate text sequences, such as in language translation or text generation tasks. The key takeaway is that Transformers are an architecture, not a specific model, and different models can utilize various parts of the Transformer architecture to solve different problems.",
  "summary": "Before jumping into APIs, RAG, agents, and AI applications, I wanted to understand what actually happens inside an LLM. I kept coming across terms like neural networks, deep learning, Transformers, attention, tokens, embeddings, BERT, GPT, and causal language modeling — but they all felt like disconnected pieces. I could understand each concept individually, but I didn't have a clear picture of…",
  "key_points": [
    "Neural networks learn patterns from data without explicit programming",
    "Transformers address RNN/LSTM limitations with attention mechanisms",
    "Encoder-only models (BERT) and decoder-only models (GPT) serve different tasks"
  ],
  "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."
}