{
  "$type": "site.standard.document",
  "bskyPostRef": {
    "cid": "bafyreibt7raw6s36yzi6h3ahwj43j6wjm3gz6yxxnhexlmukibycwksupi",
    "uri": "at://did:plc:pgryn3ephfd2xgft23qokfzt/app.bsky.feed.post/3mjs6jzksczk2"
  },
  "path": "/t/why-llm-agents-keep-failing-and-it-s-not-the-prompt/175361#post_2",
  "publishedAt": "2026-04-18T18:49:54.000Z",
  "site": "https://discuss.huggingface.co",
  "textContent": "### The Real-World Rules for AI Stability\n\nI’ve tested countless models, and while the tech is amazing, it’s far from easy. Most people fail because they treat prompts like magic spells. Once you understand these 5 rules, the “brain fog” disappears:\n\n**1. Stop Over-Prompting (The “Less is More” Rule)** Long, complex prompts often cause “attention drift.” The AI starts overthinking the instructions and forgets the goal. Instead of one giant prompt, use a clear structure and give the model one task at a time.\n\n**2. Never Drop Below Q4 Quantization** Using Q2 or Q3 models is the fastest way to get hallucinations. These “thin” models lack the weights to hold complex logic. Use **Q4_K_M** or higher—it’s the “sweet spot” where the AI stays grounded and reliable.\n\n**3. Provide “Grounding” via API (e.g., Brave Search)** An AI without a data source is just a “hallucination machine.” Connect it to a search API with a strict token limit. Real-time data keeps the agent honest and prevents it from making things up when it doesn’t know the answer.\n\n**4. Filter for Stability, Not Hype** Don’t chase every new “benchmark king” on Hugging Face. Check the download counts and user feedback. A stable, older model is always better for an agent than a flashy new one that crashes under pressure.\n\n**5. Research the Model’s “Ceiling”** Every model has a limit. Find out what it _can’t_ do before you start. For example, never give an AI unsupervised access to your system files—always set boundaries and keep a “human in the loop” to verify its actions.",
  "title": "Why LLM agents keep failing (and it’s not the prompt)"
}