{
  "id": 10761365,
  "title": "I Made an LLM Read My PDFs Without Fine-Tuning It",
  "url": "https://urgent.news/2026/09/29/i-made-an-llm-read-my-pdfs-without-fine-tuning-it",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-29T19:16:26.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/utsav_d_ae8a63f5daa5b5c23/i-made-an-llm-read-my-pdfs-without-fine-tuning-it-58e2"
  },
  "original_language": "en",
  "account": "A researcher described how to create a PDF chatbot that answers questions without fine-tuning a large language model (LLM). The process involves several key steps: extracting the PDF's text, breaking it into smaller chunks, converting those chunks into numerical embeddings, storing the embeddings in a vector database, and then using a user's question to retrieve relevant chunks from the database. These chunks are then fed to the LLM alongside the user's question, enabling the model to generate a relevant answer based on the provided context. This approach, known as Retrieval-Augmented Generation (RAG), allows the LLM to focus on the most pertinent parts of the document, improving efficiency and accuracy compared to processing the entire document at once.",
  "summary": "I Built a PDF Chatbot Without Fine-Tuning an LLM — Here's How It Works I had a simple problem. I had a PDF containing a lot of information, and I wanted to ask questions about it. Something like: \"What are the main findings?\" \"What dataset was used?\" \"Explain the methodology in simple terms.\" \"Where does the paper discuss its limitations?\" My first thought was: Do I need to train an AI model on…",
  "key_points": [
    "Extract PDF text and split into chunks",
    "Convert chunks to numerical embeddings",
    "Use Retrieval-Augmented Generation (RAG)"
  ],
  "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."
}