While low-code tools simplify

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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 accountable 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, 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.

Why it's important to focus on china mobile database and diverse datasets
LLMs rely heavily on the quality and diversity of training data. If the data is biased or not diverse enough, the model’s output may exhibit bias or reinforce stereotypes, Mirabile warns. Biased results can lead to unfair or undesirable consequences, especially in applications involving sensitive topics or diverse user bases.

Also, as Ebenezer Schubert, vice president of engineering at OutSystems, points out, LLMs can hallucinate. “The prompt that you use for LLMs can be hacked if you’re not careful. If you’re doing any fine-tuning based on your interactions and not paying attention to the data set, that can also lead to negative learning effects. These are all things to watch out for,” he says.

However, fine-tuning LLM for specific tasks requires experience. According to Mirabile, achieving optimal performance often requires experimenting with hyperparameters and adapting the model to a specific task. “Insufficiently fine-tuned models can result in suboptimal performance or difficulty adapting the model to specific use cases,” he says.