Ultimate guide for designing your GPT-3.5-Turbo model

Master of Code has been using OpenAI’s GPT-3.5-Turbo model ever since it was released. After numerous successful implementations, we wanted to share our best practices for quickly creating the model for your Generative AI solution. 

It’s important to note that, even though new models may have debuted since this article was published, these prompt engineering methods still hold true for OpenAI’s most recent model, GPT-3.5-Turbo. 

GPT prompt engineering: what is it? 

The process of strategically creating prompts to control the behavior of GPT language models, such as GPT-3, GPT-3.5-Turbo, or GPT-4, is known as GPT prompt engineering. Creating prompts that will influence the model to produce the desired responses is required. 

You can train the model to respond in a way that is more accurate and appropriate for the situation using prompt engineering techniques. Because GPT prompt engineering is iterative, careful testing, analysis, and experimentation are required in order to produce the desired results. 

Understanding the GPT-3.5-Turbo model’s fundamentals 

Understanding the settings that need to be made for the GPT-3.5-Turbo model is essential before beginning the GPT prompt engineering process. 

• Pre-prompt (Pre-header): in this field, you should enter the model’s set of rules and directives that will govern how it behaves. 
• Max tokens: This restricts how long the model’s responses can be. For reference, 100 tokens equate to approximately 75 words. 
• Temperature: The temperature of your chatbot affects how dynamic its responses are. For more dynamic responses, set it higher, for more static ones, lower. A value of 0 indicates that it will always produce the same result. A value of 1 on the other hand greatly increases the model’s creativity. 
• Top-P: It determines how extensive the chatbot’s vocabulary will be, much like temperature does. For best results, we advise a top P of 1 and temperature adjustment. 

The chatbot’s answer’s repetition of certain words is regulated by the presence and frequency penalties. Our tests show that it is best to keep these parameters at 0. 

• Setting your boundaries: Now that you are aware of what they are, it is time to set the values for the parameters in your GPT-3.5 model.

These are the things that have so far worked for us: 

• Temperature:  We advise using a temperature of 0.7 or higher if your use case permits high variability or personalization (such as product recommendations from user to user). If you prefer more static responses, such as answers to frequently asked questions about shipping rates or return policies, set it to 0.4.  When choosing your parameters, keep in mind that when the temperature metric is higher, the model tends to add 10 to 15 words on top of your word/token limit. 
• Top-P: We suggest keeping this at 1 for the best outcomes. Change your thermostat instead. 
• Max tokens: Select a number between 30 and 50 for succinct, concise responses (which, in our experience, are always the best), depending on the main use cases for your chatbot. 

Creating the pre-prompt for the GPT-3.5-Turbo model.

In this circumstance, you get to put your quick engineering skills to use. Because Conversational AI bots already use other types of prompting, the pre-prompt is also referred to as the pre-header at Master of Code. The following is a list of suggestions for writing your pre-header. 

• Setting up your model’s context is one of the first steps in prompt engineering. As a starting point, give your bot a name, personality quirks, voice tone, and behavior. Give your robot a personality next. Include the use cases it must support as well as its scope. 
• When giving instructions, use ‘Do’ rather than ‘Do not’ rather than the opposite.
• In your pre-header instructions, give examples to make sure intent is correctly discerned. To achieve the desired behavior, experiment with various synonyms.

While performing prompt engineering, refrain from giving instructions that are contradictory or redundant. Choose the most words you’d like the bot to use when responding. As an illustration, consider the sentence that follows: “When the dialogue begins, you introduce yourself by name and then proceed to assist the user in no more than 30 words.” Output length control is a key component of prompt engineering because you don’t want the model to produce excessively lengthy texts that nobody reads.

Pre-header orders should be placed by using:  The bot’s persona, purpose, and instructions. 

For prompt engineering, communication between your Conversation Design and AI Trainer teams is crucial. 

Testing your GPT-3.5-Turbo model

Now that your GPT-3.5 model has been prepared for success, it’s time to put your quick engineering skills to the test. You’ll need to test and modify your pre-header because, as was already mentioned, prompt engineering is iterative, and the model probably won’t behave as you intended it to the first time. Here are some simple yet important guidelines for reviewing and updating your pre-header: 

Test every small change: Testing is essential because even the smallest change could cause your model to behave differently. You should test everything, including the comma you added in the middle, before making any further changes. 

Remember that you can change other elements as well, like temperature. You must do more than just edit the preheader to get the desired output. 

Finally, don’t be surprised if there are a few instances in your testing data where the bot behaves differently, even if you think you have perfected the prompt engineering of your pre-header, and the bot is behaving as you would expect it to. 

This is a result of generative AI models’ unpredictable behavior.

To combat this, we administer injections. These have the power to modify the behavior of the model and override pre-header directives. They are sent to the model from the backend as an additional guide and can be used at any point during the conversation. 

EndNotes…

By following these GPT prompt engineering best practices, you can enhance your GPT model and obtain more accurate, personalized responses for your specific use cases.