{
  "url": "aeoptimizer.com/faq/what-should-we-prioritize-if-we-have-to-improve-our-llm-s-performance-urgently-i",
  "name": "What should we prioritize if we have to improve our LLM’s performance urgently in the next few weeks?",
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      "name": "What should we prioritize if we have to improve our LLM’s performance urgently in the next few weeks?",
      "@type": "Question",
      "acceptedAnswer": {
        "text": "For an urgent timeline, focus on the highest‑leverage changes: better prompts, sampling parameters, and simple retrieval from existing systems. Avoid deep fine‑tuning and major infra overhauls; they won’t fit a short window.\n\nShort projects should prioritize reliability and quick wins over architectural perfection.\n\nKey factors:\n- Deadline and deployment constraints.\n- Current quality vs. required threshold.\n- Ease of integrating retrieval into existing apps.\n- Ability to roll back if a change misbehaves.\n\nPractically, run a fast diagnostic, fix obvious prompt issues, lower overly creative sampling settings, and wire the model to a knowledge base using off‑the‑shelf RAG components. Track a few key metrics and stop when they meet minimum requirements.\n\nOnce the urgent deadline passes, you can revisit deeper optimization options more carefully.",
        "@type": "Answer",
        "description": "On an urgent timeline, prioritize quick wins: clean up prompts, lower overly creative sampling settings, and add simple retrieval from your existing knowledge base. Skip deep fine‑tuning and big infra changes; aim to reach minimum reliability and latency targets fast, then refine later."
      }
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  "description": "On an urgent timeline, prioritize quick wins: clean up prompts, lower overly creative sampling settings, and add simple retrieval from your existing knowledge b"
}