Gartner predicts that by 2027, companies will use smaller AI models that focus on specific tasks at least three times more than general-purpose large language models (LLMs). Large language models can understand and generate text well, but they may struggle with tasks that need specific knowledge about a business area.
Sumit Agarwal, a VP Analyst at Gartner, explained, “Since businesses have many different tasks and need more accurate responses, there is a movement toward using specialized models that are designed for specific tasks. These smaller models give quicker answers and need less computer power, which helps save money on operations and maintenance.”
“Small, particular tasks models offer quicker responses and use less computational power, reducing operational and maintenance costs.” – Sumit Agarwal.
Businesses can customize large language models for specific tasks using techniques like retrieval-augmented generation (RAG) or by fine-tuning them with their own data. Such customization requires careful preparation of data with quality and proper management for the models to work well.
Here are some tips for companies looking to use these smaller, specialized AI models:
1. Test out Contextualized Models: Start with small, context-focused models in areas where knowing the business context is very important or where large language models haven’t worked well.
2. Use a Combination of Models: Look for jobs where one model alone is not enough. Instead, use different models together, along with multiple steps in the workflow.
3. Prepare Data and Build Skills: Focus on collecting and organizing the right data needed to fine-tune the language models. Also, invest in training staff in different roles, such as AI specialists, data scientists, and business experts, so they can support these projects effectively.
Also Read: Soft Skills and Technical Know-How: A Winning Combination in the Tech Industry
