A developer discovered that their browser agent's slowness was not due to the AI model (GPT-5 or Claude Opus 4.6) but rather to excessive and repetitive data being fed into the prompt. This "prompt bloat," including full page dumps, screenshots, and repeated instructions, significantly increased latency, cost, and unreliability. The developer suggests that optimizing prompt structure for caching, by placing stable instructions at the top and dynamic content lower down, can dramatically improve performance without needing to switch to a different model. AI
IMPACT Highlights that prompt engineering and context management are critical for efficient LLM agent performance, potentially more so than model choice.
RANK_REASON Developer's personal experience and advice on optimizing LLM prompts for performance.
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