Researchers from TNG Tech have found that long prompts in large language models (LLMs) can significantly block other requests, leading to performance degradation. This is because LLMs use a technique called 'prompt interleaving' where multiple requests are processed simultaneously, but long prompts can monopolize the model's resources, slowing down other tasks. The team has developed an optimization technique that separates long and short prompts, improving overall system performance by up to 15%. According to the researchers, this issue is particularly relevant for applications with high request volumes, such as chatbots or virtual assistants. --- Why it matters: This matters because it highlights a common bottleneck in LLM-based systems: the interaction between prompt length and model performance. Optimizing prompt interleaving can lead to significant improvements in system efficiency, making these models more suitable for demanding applications. Source: https://huggingface.co/blog/tngtech/llm-performance-blocked-by-long-prompts