{
  "id": 13244513,
  "title": "What are LLMs and How to Build Stuff Using it?",
  "url": "https://urgent.news/2026/10/09/what-are-llms-and-how-to-build-stuff-using-it",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-09T22:52:19.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/0xadityaa/what-are-llms-and-how-to-build-stuff-using-it-4l68"
  },
  "original_language": "en",
  "account": "Large Language Models (LLMs) are sophisticated AI systems that process and generate human-like text. Built upon the Transformer architecture, LLMs analyze extensive text datasets to grasp language patterns, context, and semantics. This enables them to excel in various tasks, such as text generation, translation, and summarization.\n\nTokenization is the initial step in LLM processing, where text is divided into smaller units called tokens. These tokens can be words, subwords, or even individual characters, depending on the tokenizer employed. Embeddings represent words or phrases as numerical vectors, capturing semantic relationships between them. This allows LLMs to understand contextual similarities and generate more meaningful responses.\n\nContext window denotes the maximum amount of text an LLM can process at once. A larger context window means the model retains more information from earlier text, leading to more coherent responses. However, exceeding the context window may result in losing crucial information from earlier parts of the conversation.\n\nTemperature controls the randomness of the model's responses. Higher values yield more creative and diverse outputs, while lower values make responses more predictable and deterministic. Fine-tuning an LLM involves training it on a specific dataset, enhancing its performance on specialized tasks and making it more domain-specific, precise, and reliable.\n\nDespite their strengths, LLMs have limitations. They can produce hallucinations, have a short memory, and struggle with logical reasoning. They are also prone to bias and can be expensive and energy-intensive to run. To harness LLMs for meaningful applications beyond simple conversation bots, developers can employ tools like LangChain.\n\nLangChain is a robust framework that simplifies the integration of LLMs with external data sources, databases, and APIs. It offers pre-built tools for memory management, chaining multiple LLM calls, and leveraging external tools like search APIs. This streamlines the process of creating complex AI-driven workflows, making AI development more scalable and efficient.\n\nHowever, LangChain has its limitations as well. While it structures AI interactions and workflows, it still relies on the inherent constraints of LLMs, such as context window size and lack of long-term memory. For more sophisticated, multi-step tasks, developers can turn to LangGraph, a powerful framework designed for creating dynamic AI applications.",
  "summary": "Ever wondered how AI models like ChatGPT, Claude, Gemini, Deepseek, Llama etc. understand and generate human-like text? The idea this whole post hangs on: an LLM only predicts the next token, so every useful application is the scaffolding you build around it: what context it sees, which tools it can call, and what it remembers. Large Language Models (LLMs) are advanced AI systems designed to…",
  "key_points": [],
  "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."
}