"Enterprise AI tends to default to large language models (LLMs), overlooking small language models (SLMs). But bigger isn't always better. Often, a smaller, more specialized model can do the work faster and more efficiently. What complicates things is that neither an LLM nor an SLM alone may give you everything you need, especially in complex enterprise environments. In both cases, structure is essential. That's where knowledge graphs come in. Knowledge graphs add the context and connections that make these models truly useful."
"Let's start with SLMs versus LLMs. Developers were already warming to small language models, but most of the discussion has focused on technical or security advantages. In reality, for many enterprise use cases, smaller, domain-specific models often deliver faster, more relevant results than general-purpose LLMs. Why? Because most business problems are narrow by nature. You don't need a model that has read TS Eliot or that can plan your next holiday. You need a model that understands your lead times, logistics constraints, and supplier risk."
Enterprises often default to large language models but smaller, domain-specific models frequently deliver faster, more relevant results for narrow business problems. Small language models excel when grounded in organizational context like lead times, logistics constraints, and supplier risk. Reasoning systems use modular, specialist components to solve targeted problems efficiently. Deploying multiple specialized models with a generalist coordinator mirrors enterprise operations and improves relevance and efficiency. Neither LLMs nor SLMs alone suffice for complex environments; explicit structure and connected context are necessary. Knowledge graphs provide that structure by encoding entities, relationships, and context, enabling models to deliver actionable, grounded outputs.
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