## SLM Prompting: A Challenge A user on the LocalLLaMA forum raises a crucial question: how can we make small language models (SLMs) outperform large language models (LLMs) in a specific domain? The question arises from the observation that, although an SLM fine-tuned for a niche area (e.g., Australian tax law) may potentially provide superior results, achieving this result through prompting proves problematic. ## The Problem of Incoherence The standard prompting approach, effective with LLMs, often fails with SLMs. Even when the prompt concerns the topic on which the SLM has been optimized, the response may be incoherent. This suggests that the current understanding of prompting techniques may be inadequate to fully exploit the potential of SLMs. ## Rethinking Prompting The discussion implies the need for a fundamental shift in how we interact with these smaller models. Perhaps more targeted, contextualized, or structured prompting strategies are needed to guide SLMs towards coherent and accurate responses. Research into new prompting techniques could unlock the true potential of SLMs in specific areas of expertise.