How to Get Started with LLM

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In traditional programming, when a coder uses a system or API with known capabilities, the input and expected output can be documented. “You can be confident in what output to expect because the system, while not necessarily stable, will be deterministic and known in advance,” says David Colwell, vice president of AI and machine learning at Tricentis

But learning systems, by their very nature, violate this paradigm. “It’s not a bug, it’s more a feature of their nature,” Colwell says. “They learn from huge amounts of data, but we don’t expect them to get it right; we just expect them to get better over time.”
Sometimes improvement in one area may come at the expense of another, and this is perfectly acceptable.

Colwell says AI systems shouldn’t be blamed or held senegal mobile database for being non-deterministic. Instead, he recommends that IT leaders take the time to acknowledge the technology’s limitations.

Andrea Mirabile, global director of AI research at Zebra Technologies, notes that working with LLM often requires a deeper understanding of machine learning algorithms. However, he says, while it’s useful for the average programmer to have a basic understanding of ML concepts, some tools and frameworks offer a more accessible entry point.

In Mirabile’s experience, understanding issues such as model fine-tuning, hyperparameter tuning, and the nuances of working with training data can help achieve optimal results. Low-code tools are also helpful. He suggests that IT decision makers consider how they can use low-code tools to create a more user-friendly interface for those without ML experience. As an example, he cites LangChain, a platform that offers developers tools for rapid prototyping and experimentation with LLM.

However, while low-code tools simplify some aspects of developing LLM-based AI applications, Mirabile cautions that they may have limitations in supporting highly specialized tasks or complex model configurations. In addition, developers need a fundamental understanding of ML to make informed decisions about model behavior.

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