{
  "id": 4730593,
  "title": "Why more context makes your AI answers worse",
  "url": "https://urgent.news/2026/08/31/why-more-context-makes-your-ai-answers-worse",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-31T22:16:08.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/vladut02/why-more-context-makes-your-ai-answers-worse-555o"
  },
  "original_language": "en",
  "account": "The article titled \"Why more context makes your AI answers worse\" discusses the limitations of AI models regarding their context window. It clarifies that the context window is not a measure of memory, as the model does not retain any information between calls. Each call requires sending the entire conversation again.\n\nThe model's attention mechanism, which determines how much each token looks at other tokens, is highlighted as the primary factor impacting performance. As the context window increases, the number of attention pairs grows exponentially, making the workload significantly higher. This results in slower responses initially, but once the model caches the keys and values, subsequent responses are faster. However, this does not improve the quality of the answers.\n\nThe article identifies three reasons why large context sizes negatively affect AI model performance:\n\n1. Attention is a budget: Attention weights sum up to one for each token, so adding more context splits the same focus across more pieces, reducing the model's ability to focus on the relevant information.\n\n2. Lost in the middle: The position of important information within a long prompt significantly impacts accuracy. Information near the beginning and end of the prompt performs well, while information in the middle performs much worse.\n\n3. Distractors in your own data: When dealing with real-world data, the model faces challenges due to the presence of multiple similar chunks, some of which may contain incorrect information. This makes it difficult for the model to determine the correct answer, and the accuracy steadily decreases with input length.\n\nTo mitigate these issues, the article provides four recommendations:\n\n1. Send less, but better: Instead of sending a large chunk of context, focus on retrieving and sending the most relevant information, such as top 3-5 chunks after searching and re-ranking.\n\n2. Use the edges: Place the actual question at the bottom of the prompt, after providing the necessary context at the top and middle. This ensures the model focuses on the relevant information without wasting attention on irrelevant parts.\n\n3. Stop reusing one giant thread: Break down the task into smaller steps, with each step receiving a fresh, small context containing only the necessary information. Pass a short summary between steps to maintain context without overwhelming the model.\n\n4. Measure on your own data: Conduct experiments using real questions from your application, comparing the performance of the model with different context sizes. This allows you to determine your specific model's limit and optimize accordingly.\n\nUltimately, the article emphasizes treating the context window as a limited resource and advises users to send the smallest and most relevant context required to answer the question effectively.",
  "summary": "Your model says it has a one-million-token context window. Its real working memory is a lot smaller than that. On long-context benchmarks, models start failing well below the number printed on the box. And here's the part nobody warns you about: past a certain point, adding more context makes your answers worse . Not better. Worse. Prefer to watch? Full walkthrough with the attention-cost…",
  "key_points": [
    "Attention mechanism determines token focus; larger context splits focus, reducing performance",
    "Important information near beginning and end of prompt performs best, middle performs worst"
  ],
  "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."
}