Unlocking the Power of Small Language Models for Organizations

Artificial intelligence is changing fast, and small language models (SLMs) are becoming more important for many companies. But why should we care about these smaller models when big ones seem to do everything? To answer that, we need to understand how models use Fine-Tuning vs. Context: A Simple Cooking Analogy

Let’s compare this to cooking. Think of a chef who’s cooked for many years. They know how flavors mix, how long to cook different meats, and how to fix things if something goes wrong. That’s like a fine-tuned model. It has learned from past experiences and knows how to respond well without needing extra help.

Now imagine giving a new cook a recipe. They can follow it step by step, but if the recipe is unclear or missing something, the dish might not turn out great. That’s what it’s like when a model depends only on context. It works with what you give it right then, but if that information is incomplete, the result won’t be as good.

Why Context Isn’t Always Enough

When a model only uses context, it depends fully on what we give it at the time of the question. If we forget something important or don’t phrase it well, the answer may be wrong or just not very helpful. Also, the person or system creating the context might miss things without realizing it.

So Why Use Small Language Models?

Small models are great for specific tasks. They can be trained with less data and made to understand just one area of a business really well. Imagine a chatbot that only handles your company’s support questions. It doesn’t need to know about the whole internet—just your products, your tone, and your most common issues.

They also work well when you need fast responses, want to save money, or must keep data private. Small models can run directly inside your company’s systems, without sending information to the cloud. They’re also easier to update when things change.

A Smarter Setup: Many Small Models

Instead of one big model doing everything, you can build several small ones. Each one is trained for a specific area like customer support, IT help, or HR questions. This makes it easier to manage them, and if something changes in one area, you only need to retrain that specific model.

Example: Support Assistant at Jabra

Let’s say Jabra builds a support assistant.

• If they fine-tune a small model using real support tickets and product details, it will give fast and accurate answers.
• If they use a model that depends only on context, they need to provide all that information every time. If something is missing or outdated, the answer might not be great.

The fine-tuned model is like a smart support expert. The context-only one is more like a new intern reading from a manual. Both can help, but only the fine-tuned model really “gets it.”

Using Both Approaches Together

Often, the best solution is to use both fine-tuning and context. Fine-tuning gives the model a solid foundation. Then context can add new or temporary information. This way, the model is smart and flexible at the same time.

What’s Next: Building the Right Pipelines

To get the most out of small models, companies should build pipelines that:

• Collect useful, domain-specific data
• Fine-tune models with that data
• Deploy models into the tools people use
• Collect feedback and keep improving them

In the next blog, we’ll talk about how to set up these systems so your small models keep getting better over time.

Stay tuned!