Hi there. My name is Ash and I'm a developer relations engineer at IBM. I've been working on tools around AI and rag for the last couple of years and I've got something I'd like to show to you today.Now first things first, I've heard that rag is dead many a time and I'm sure you have too. Context windows are huge these days, so you might as well just dump all of your information into there. I don't take this kind of thing very seriously.If every business has less than a million tokens worth of data then sur ...

Core Insights - The belief in large language models (LLMs) is declining as the industry shifts focus towards smaller, more tailored models that meet specific business needs [1][2] - The latest release of ChatGPT-5 has not generated as much excitement as the iPhone 17, indicating a potential stagnation in LLM advancements [1] - Companies are increasingly favoring small language models (SLMs) due to their cost-effectiveness and efficiency in specific applications, such as human resource management [1][2] Group 1 - The comparison of LLMs to early smartphones highlights that while initial releases were revolutionary, current iterations resemble minor upgrades [1] - SLMs are gaining traction in enterprises as they are easier to deploy and less costly, making them more appealing for specific tasks [1][2] - The rise of SLMs is driven by the need for models that can operate efficiently within existing IT systems and devices sensitive to energy consumption [1][2] Group 2 - There is no clear definition of "small language models," but they typically have fewer training parameters compared to LLMs, with some models having as few as 100 million parameters [2] - The demand for SLMs is expected to grow at twice the rate of LLMs this year, driven by user fatigue with LLM issues like "AI hallucinations" [2] - SLMs can perform standardized tasks without the resource demands of LLMs, making them a more economical choice for businesses [2] Group 3 - SLMs are positioned to become central to "agent-based AI," allowing for cost-effective task completion and modular combinations of specialized models [3] - While LLMs will continue to dominate consumer applications, SLMs are likely to be more prevalent in enterprise and device-level AI solutions [3] - OpenAI is also utilizing models of varying sizes internally to allocate resources based on task complexity [3]