What Is OpenAI Temperature?

OpenAI temperature allows users to control the predictability of ChatGPT’s responses. Ranging from zero to two, the temperature parameter determines the creativity or predictability of each response.

In practice, OpenAI temperature can help your ChatGPT’s output create a beautiful haiku, find the right wording for social media posts, or draft a sympathetic email that mitigates customer concerns. 

Explore how mastering OpenAI temperature may help you customize ChatGPT for professional and educational development.

Types of OpenAI temperature

While OpenAI temperature can be set at any number between zero and two, you may prefer to use different settings or temperature types in this range for a variety of creative and professional applications.

OpenAI temperature is similar to predictive texting. At zero, the AI will choose the most likely answer to your prompt. At the higher end of the zero to two scale, its results will be more random. 

In practice, this means you’ll likely get very rigid results with a setting at or near zero. On the other hand, you may notice more creative results—which may become hallucinations (misleading or incorrect results)—if the temperature is set closer to two. This is why, generally speaking, OpenAI sets its creative AI responses to default at one. An OpenAI temperature of one gives you the necessary creativity to write a beautiful haiku and the factual accuracy and coherence to draft a technical manual. However, if you’re using the application programming interface (API), you can choose any temperature from zero to two.

The OpenAI temperature you choose to use will depend entirely on your situation. For tasks that rely on a high degree of predictability—like spell-checking, translation, or mathematical calculations—it’s best to set the temperature to a value that’s a fraction above zero. For tasks that require more creativity—like social media posts or marketing materials—it’s best practice to set the temperature closer to two.

What is OpenAI temperature used for?

Professionals spanning multiple fields are now integrating OpenAI temperature into their work. These professionals often use OpenAI temperature to control the creativity or rigidity of responses to their AI prompts. Because large language models (LLMs) are stochastic, or random, by nature, every response will vary slightly. Controlling the temperature allows you to make the LLM’s output a bit more predictable.

OpenAI temperature use case example

One notable study demonstrates how fine-tuning OpenAI temperature can be valuable to professionals across multiple industries, including health care. The March 8 edition of the scholarly journal JMRI Human Factors published a study that encourages health care professionals to incorporate variation in the temperature settings into their research and public-facing communication [1].

The study consisted of an experiment to test and demonstrate the deterministic output of various OpenAI models. Joshua Davis et al. created three prompts and ran them 10 times across three temperature settings: 0 (low), 0.5 (medium), and 1 (high).

To test the variability in creativity across these three temperatures, Davis et al. fed the models an abstract from a recently published scientific paper. Then, they prompted Text-davinci-003, ChatGPT-3.5, and ChatGPT-4 Preview to:

• Create a tweet

• Compose a title for a scientific journal

• Draft a title for a keynote address

Interestingly, they found that temperature settings affected the results from newer models—GPT-3.5 and above—less than Text-Davinci-003. Even when set to zero, GPT-3.5 and GPT-4 Preview showed some randomness in their outputs [1].

The difference in results occurred because developers created base completion models, like Text-Davinci-003, to complete the sequence of words. In essence, they were like a more complex version of predictive texting. However, newer AI models, like ChatGPT-3.5 and beyond, are chat-based, so they’re designed to answer questions. Thus, they’re less likely to generate overly creative or random output.

With that being said, the general principle still holds: Lower temperatures produce more deterministic outputs than higher ones. Understanding these facts may help professionals in many STEM fields communicate better with the general public. 

How do OpenAI temperature, RAG, and prompt engineering impact creative AI responses?

While OpenAI temperature affects how creative or predictable an AI’s output will be, two other factors may also impact the results: retrieval-augmented generation (RAG) and prompt engineering.

First, RAG occurs when an LLM augments its generated text with retrieved information from another source, e.g., an encyclopedia or the internet. RAG models, like SearchGPT, provide you with relevant, up-to-date, reliable information and are less prone to hallucinations because they retrieve information from external, real-time sources.

Along with optimizing the temperature for your specific application and using a RAG model, writing well-engineered prompts will also make your results more accurate and less creative. 

Write prompts that are:

• Clear

• Precise

• Contextual

• Role-specific

• Direct

In short, you should attempt to clearly detail the kind of output you want to achieve by using a variety of techniques, including OpenAI temperature, RAG, and prompt engineering. 

Who uses OpenAI temperature?

OpenAI temperature plays a significant role in helping professionals, educators, and students learn, teach, and market more effectively.

As an example, graduate students in education at Old Dominion University have used ChatGPT, role prompting, and temperature to analyze their work conversationally. By instructing ChatGPT to stand in as a debate partner or some other persona, graduate students can explore their arguments’ strengths and weaknesses and gain perspective on their work.

Marketers will often set ChatGPT to a temperature of around one to create customer experience surveys. During a product launch, social media marketing professionals may use ChatGPT to create engaging, promotional social media captions or posts quickly and efficiently.

Educators are also leveraging ChatGPT to help students connect and engage with content, including Shakespeare’s work. Cara Beloate, an English Language Arts teacher, encourages her students to use ChatGPT to translate Shakespeare’s plays, such as Othello, into modern English. This frees up class time for deeper discussion and exploration of the themes and plots.

Beyond the classroom, entrepreneurs looking to delve into the world of e-commerce may use ChatGPT to conduct initial market research. ChatGPT’s newly integrated SearchGPT feature may be especially helpful for marketers conducting preliminary market research into:

• Basic customer information, such as age, location, and income

• Customer interests, values, and lifestyle preferences

• Customer challenges and needs

After gathering the data, ChatGPT can help marketers create a marketing strategy based on those insights.

Next, explore the industries and vocations that may benefit from OpenAI temperature, along with their salaries and projected job outlook.

Marketing analysts

Marketing analysts use statistical and demographic data to:

• Forecast and monitor marketing trends

• Analyze and measure current marketing strategies

• Develop surveys and other data collection tools to gain insight into current market conditions

• Collect and analyze statistical data on current market and consumer trends

• Create graphs and reports using collected data

• Present results to clients

Between the years 2023 and 2033, the US Bureau of Labor Statistics (BLS) projects that the need for marketing analysts will grow by 8 percent [2]. By contrast, BLS projects that the growth of all occupations in the US economy will be only four percent over the same period. 

The typical salary for marketing analysts stands at $74,680, while the average salary for all occupations is $48,060 [3].

The growing demand for data-driven insights on consumer behavior has led to this demand for marketing professionals, and marketing specialists in general. Businesses across multiple industries are increasingly turning to data derived from online product reviews and social media posts to determine what consumers want and need. If you love data and understanding why consumers make the purchasing decisions they make, then you’ll likely have plenty of opportunities to shine as a marketing professional.

Professors

As a professor, you will:

• Teach at public and private colleges and universities

• Create course outlines for classes

• Grade student exams and assignments

• Collaborate with departmental colleagues to develop curricula for degree programs in your specialty

• Publish research in your specialty

• Mentor and supervise graduate students

• Design and conduct experiments

Like marketing analysts, the job outlook for both adjunct and tenured professors is strong. The occupation as a whole is projected to grow by eight percent between 2023 and 2033. Additionally, nearly 119,000 jobs will become available annually over the next decade [4].

In addition to the high demand for post-secondary instructors, you can expect to earn more than the average employee. In total, the average US employee earns a median salary of about $48,060, while post-secondary instructors, on average, earn $84,380, nearly twice the national average for all occupations [5].

Salaries for post-secondary educators will likely vary, based on the area specialty, whether the professor teaches full or part-time, and where they teach. That is why salaries for professors can range from below $49,440 to over $182,700. However, post-secondary instructors will likely experience many opportunities for career growth and a strong job market over the next decade.

Therefore, if you're interested in achieving a doctoral degree and in empowering students to continue learning beyond high school, then you may want to consider a career as a professor.

Customer service personnel

Beyond individual career applications, OpenAI temperature has had a broad impact on multiple industries, especially those that provide client support. 

For example, when incorporated with a RAG model, such as SearchGPT, OpenAI temperature can generate responses that are tailored to the customer’s query. Instead of creating generic responses, ChatGPT can help a customer troubleshoot a technical issue, using external web or knowledge base sources for improved accuracy.

OpenAI temperature also shines in sentiment analysis. Professionals in many industries may use sentiment analysis to collect data about customers’ feelings and attitudes toward a product or service and use that data to de-escalate customer concerns. Mining customer inputs for clues about their underlying feelings toward the product/service allows you to observe trends over time and determine areas for improvement.

Pros and cons of using OpenAI temperature

OpenAI temperature is a powerful tool with myriad applications across our personal and professional lives. However, as with any new technology, you should critically assess its benefits and potential risks. 

Let’s delve into the pros and cons of using OpenAI temperature, which may help you calibrate this setting according to your unique needs.

Advantages

Mastering the basics of OpenAI temperature can help you save time and energy when completing tasks like content creation or social media marketing. 

Whether you’re working on a technical report or a casual blog post, ChatGPT can help you create a rough outline, design bullet points, and proofread your final draft for style and tone.

Disadvantages

Unless you’re using a RAG model, ChatGPT can still hallucinate, even at lower temperature settings. Without access to real-time data, the LLM cannot verify its output with external sources. The hallucination is a product of the model’s limited training data, not the the temperature setting.

Getting started in OpenAI temperature with Coursera

Mastering OpenAI temperature is part of the broader field of generative AI. Learning how to calibrate OpenAI temperature across various applications may help add to the tools and confidence you need to get started in this field. 

If you’re ready to put OpenAI temperature settings, RAG, and prompt engineering to use in the real world, then consider the OpenAI GPTs: Creating Your Own Custom AI Assistants course on Coursera.

Offered by Vanderbilt University and taught by Dr. Jules White, this seven-hour course is designed to empower you with all the tools to:

• Program a GPT with custom prompts

• Use a RAG model

• Build a persona for your custom GPT

• Incorporate tools into your custom GPT

• Understand how to verify your custom GPT's output

• And much more