Understanding AI temperature: Why ChatGPT isn’t an automation tool

ChatGPT is not an automation tool in itself. For automation, use Excel formulas, Python, R, JavaScript, or other tools designed for specific, well-defined tasks. These tools perform steps with no variance, while ChatGPT operates on a concept called temperature, which is fundamental to how large language models (LLMs) work. Temperature determines the creativity of the model and indirectly affects the consistency and quality of responses. Let’s dive deeper into why this concept is so important and why it makes LLMs unsuitable for certain automation tasks.

How Temperature Works

Temperature in large language models (LLMs) like ChatGPT is a parameter that controls the randomness of the model’s outputs. A low-temperature setting (close to 0) makes the model’s responses more deterministic and predictable. At low temperatures, the model is more likely to choose the highest probability words, resulting in outputs that are repetitive and adhere closely to common patterns seen in the training data. This means the text generated is coherent and consistent but may lack creativity and variation.

On the other hand, a high-temperature setting introduces more randomness into the model’s responses. At higher temperatures (close to 1), the model samples from a wider range of possible words, including those with lower probabilities. This increases the diversity and creativity of the output, making the text more varied and potentially more interesting. However, this also comes with a trade-off: higher temperature settings can lead to less coherent and more unpredictable text.

Understanding this balance is crucial for optimising AI applications across various fields, from automated reporting to creative writing. Lower temperatures are best for scenarios where accuracy and consistency are paramount, while higher temperatures are suited for tasks that benefit from novelty and diversity. This nuanced understanding of temperature helps clarify why LLMs like ChatGPT may not be the best fit for pure automation tasks. But this does not mean it cannot be used in the automation process.

Using LLMs in Automation

With API connections to services like ChatGPT, the model can be very useful in certain steps of the automation process. Since the model can analyse context and can be instructed less strictly than, for example, Python, it can better handle unexpected or less templated files, tasks, or documents. An example of this is translation.

In both cases, the result depends on the subjective nature of written language. If you put ten people with the same native language in a room to all translate an English text to their native language, all the translated texts will be different. This is not true with a machine translation tool like Google Translate or DeepL. These tools are designed to produce consistent translations, regardless of the text’s tone, which may read as tone-deaf to a native speaker. This sets the stage for a process where LLMs are the solution. The process needs to be quick and consistent, but there is also no defined best way to do it.

So why is this an automation process where you can use LLMs, but also a process that shows why LLMs should be used as part of the process but not the tool to automate entirely? When asked to translate large texts, ChatGPT will sometimes not translate everything in the file or will take very long to do part by part. However, a Python code that processes every Excel cell, it is a cheap, quick, and very effective translator. This demonstrates how LLMs can be implemented into automation tools to perform specific tasks efficiently.

Optimal Use of Temperature in LLMs

Temperature is what makes LLMs like ChatGPT special. It allows the model to generate responses that can vary in creativity and originality. This capability is invaluable for tasks that benefit from a degree of creative freedom, such as brainstorming, content creation, and learning. By adjusting the temperature, users can harness the model’s potential to generate novel ideas, diverse perspectives, and engaging content.

For example, in educational settings, ChatGPT can be used to explain complex concepts in multiple ways, cater to different learning styles, and provide a rich, interactive learning experience. In creative industries, LLMs can assist in writing, ideation, and artistic projects, where a higher temperature can stimulate innovative thinking and produce unique outputs.

To make the most of LLMs, it is essential to align the task with the technology behind the nice interface. At the heart of this technology is temperature, so make sure you use it for tasks that require this temperature to be there. If you find yourself trying to minimize the creativity of the model because the output is not consistent enough 10 out of 10 times, think about using a tool where there is nothing as temperature.

Putting it into practice

In conclusion, temperature is fundamental to understanding why ChatGPT and other LLMs are not ideal for pure automation but excel in tasks requiring creative freedom, such as brainstorming, content creation, and learning. Temperature controls the randomness and creativity of outputs, with low settings producing consistent responses and high settings fostering diverse text. Traditional automation tools like Python and Excel are better for tasks needing strict consistency.

However, LLMs shine when integrated into specific steps of automation processes, offering contextual understanding and varied responses. To maximise LLMs‘ potential, align tasks with their capabilities, considering temperature as a key factor. For tasks demanding minimal creativity and maximum consistency, use tools designed for strict automation. Recognising and appropriately utilising the concept of temperature will ensure your experience with using AI will be very warm.

Blog by Dennis Hulsebos