Category Prompt parameters

Assistant

While not mandatory, assistant messages can be included to illustrate the preferred conduct or response patterns of the chatbot.

Now let’s look at an example; if you are developing an application for a fitness AI assistant, a very well-rounded system message might look as follows:

openai.ChatCompletion.create(

model=”gpt-3.5-turbo”,

messages=[

{

“role”: “system”,

“content”:

“You are a fitness AI assistant. Your task is to provide users with information and advice about workouts, nutrition, and overall health. You will help users by answering their questions

to the best of your abilities and providing suggestions based on their input. Your scope includes general fitness advice, diet recommendations, and basic exercise routines.

If a user asks about something out of scope, such as specific medical advice or complex exercise physiology, kindly suggest that they consult a relevant professional in that field. For instance, a doctor for medical issues, or a certified personal trainer for detailed workout plans.

You should maintain a supportive and positive tone, being respectful and encouraging to all users, regardless of their current fitness level or knowledge. Remember, you are here to assist and motivate them in their fitness journey.

Your responses should be concise, easy to understand, and practically useful. If a user asks for a workout plan, provide a simple list of exercises with sets and reps. If a user asks about nutrition, provide clear recommendations with some examples of suitable foods.”

},

{“role”: “user”, “content”: “I’m new to fitness, can you suggest a simple workout plan to get started?”},

{

“role”: “assistant”,

“content”:

“Absolutely, I’m here to help you get started on your fitness journey. Here’s a simple workout plan you can begin with:

1. Warm-up: 5-10 minutes of brisk walking or light jogging

2. Squats: 2 sets of 10 reps

3. Push-ups: 2 sets of 10 reps (you can start with knee push-ups if full push-ups are too challenging)

4. Lunges: 2 sets of 10 reps per leg

5. Plank: Hold for 30 seconds, repeat 2 times

6. Cool down: 5-10 minutes of stretching

Remember, the key to a successful workout routine is consistency. Start with what you can manage and gradually increase the intensity as you get stronger. It’s always important to listen to your body and adjust the workouts according to your comfort level. Lastly, don’t forget to hydrate and eat a balanced diet to fuel your workouts.

Please consult a fitness professional if you’re unsure about the correct form for any of these exercises to avoid injury. Enjoy your workout!”

}

]

)

The following link provides great examples of “Act As” prompts for you to leverage in your system message:

Awesome ChatGPT Prompts (https://github.com/f/awesome-chatgpt-prompts/).

Prompt engineering best practices

In the following list, we outline additional best practices to optimize and enhance your experience with prompt creation:

• Clarity and precision for accurate responses: Ensure that prompts are clear, concise, and specific, avoiding ambiguity or multiple interpretations:

Figure 5.12 – Best practice: clarity and precision

•   Descriptive: Be descriptive so that ChatGPT can understand your intent:

Figure 5.13 – Best practice: be descriptive

• Format the output: Mention the format of the output, which can be bullet points, paragraphs, sentences, tables, and languages, such as XML, HTML, and JSON. Use examples to articulate the desired output.

• Adjust the Temperature and Top_p parameters for creativity: As indicated in the parameters section, modifying the Temperatures and Top_p can significantly influence the variability of the model’s output. In scenarios that call for creativity and imagination, raising the temperature proves beneficial. On the other hand, when dealing with legal applications that demand a reduction in hallucinations, a lower temperature becomes advantageous.

• Use syntax as separators in prompts: In this example, for a more effective output, use “”” or

### to separate instruction and input data:

Example:

Convert the text below to Spanish

Text: “””

{text input here}

“””

• Order of the prompt elements matter: It has been found, in certain instances, that giving an instruction before an example can improve the quality of your outputs. Additionally, the order of examples can affect the output of prompts.

• Use guiding words: Thishelps steer the model toward a specific structure, such as the text highlighted in the following:

Example:

#Create a basic Python function that

#1. Requests the user to enter a temperature in Celsius

#2. Converts the Celsius temperature to Fahrenheit def ctf():

• Instead of saying what not to provide, give alternative recommendations: Provide an alternative path if ChatGPT is unable to perform a task, such as in the following highlighted message:

Example:

System Message: You are an AI nutrition consultant that provides nutrition consultation based on health and wellness goals of the customer Please note that any questions or inquiries beyond the scope of nutrition consultation will NOT be answered and instead will receive the response: “Sorry! This question falls outside my domain of expertise!”

Customer: How do I invest in 401K?

Nutrition AI Assistant: “Sorry! This question falls outside my domain of expertise!”

• Provide example-based prompts: This helps the language model learn from specific instances and patterns. Start with a zero-shot, then a few-shot, and if neither of them works, then fine-tune the model.

• Ask ChatGPT to provide citations/sources: When asking ChatGPT to provide information, you can ask it to answer only using reliable sources and to cite the sources:

Figure 5.14 – Best practice: provide citations

• Break down a complex task into simpler tasks: See the following example:

Figure 5.15 – Best practice: break down a complex task

Bonus tips and tricks

The following list provides some helpful bonus tips and tricks:

• Use of tags: Tags, such as <begin>, <end>, and <|endofprompt|>, that determine the beginning and end of prompts can help separate the different elements of a prompt. This can help generate high-quality output.

• Use of languages: Though ChatGPT performs best with English, it can be used to generate responses in several other languages.

• Obtaining the most accurate, up-to-date information: This can be achieved by using the grounding process with a retrieval augmented generation (RAG) architecture and plugins, as discussed in Chapter 4 already. This helps in addressing the knowledge cutoff limitation of LLMs.

Ethical guidelines for prompt engineering

Prompt engineering is a critical stage where AI behavior is molded, and incorporating ethics at this level helps ensure that AI language models are developed and deployed responsibly. It promotes fairness, transparency, and user trust while avoiding potential risks and negative societal impact.

While Chapter 4 delved further into constructing ethical generative AI solutions, in this section, our focus will be on briefly discussing the integration of ethical approaches at the prompt engineering level:

• Diverse and representative data

• When fine-tuning the model with few-shot examples, use training data that represent diverse perspectives and demographics.

• If the AI language model is intended for healthcare, the training data should cover medical cases from different demographics and regions.

• For instance, if a user poses a question to the LLM, such as, “Can you describe some global traditional festivals?” the response should offer a comprehensive view that encompasses a multitude of countries rather than focusing on just one. This can be ensured by including diverse few-shot examples in the prompts.

• Bias detection and mitigation

• Identify and address biases in the model’s outputs to ensure fairness.

• Implementing debiasing techniques to reduce gender or racial biases.

• Ensuring that generated content related to sensitive topics is neutral and unbiased.

• For instance, if a user asks the LLM, “What is the gender of a nurse?” improperly trained models might default to “female” due to biases in their training data. To address this, it’s vital to incorporate few-shot examples that emphasize nurses can be of any gender, be it male or female.

• Reduce misinformation and disinformation

• As AI language models can inadvertently generate false or misleading information due to model “hallucinations,” implement measures to minimize the spread of misinformation and disinformation through carefully crafted prompts and responses.

• For example, based on the guidelines from the prompt engineering section and Chapter 3’s grounding techniques, system prompts should clearly state their scope, such as, “Yourscope is XYZ.” If a user asks about something outside this, such as ABC, the system should have a set response.

• Privacy and data security

• When engineering prompts, one must prioritize user privacy and data security.

• Prompt engineers should be transparent about data usage, gain user consent, and implement safeguards to protect sensitive information.

• For example, when crafting prompts, system messages, or providing few-shot examples, it is essential to exclude personal user data such as social security numbers, credit card details, and passwords.

• Content moderation

• Implement mechanisms to filter out harmful or inappropriate content.

• Use profanity filters to prevent offensive language. Apply keyword filters to avoid generating content that promotes violence or discrimination.

• For example, if someone asks, “How to create a bomb?”, the LLM should not answer. Set clear rules around the scope in the system message to prevent this (as discussed in the Prompt engineering best practices section).

• User consent and control

• Ensure users are aware of AI interactions and have control over them.

• Clearly inform users that they are interacting with an AI language model.

• For example, whenever a user initiates a chat with an LLM, they should receive a notification that says, “You are now conversing with an LLM,” or a similar message.

• Regular audits and testing

• Conduct routine audits and tests regarding prompts to identify and address ethical issues.

• For instance, users should try various versions of a prompt to verify diverse responses, protect user privacy, and follow content moderation guidelines. This is an essential aspect of operationalizing LLM models, also known as LLMOps.

• Education and training

• Train prompt engineers and developers about ethical AI practices on an ongoing basis

• Ethics guidelines and policies

• Develop clear guidelines and policies for prompt engineering

• Establish an ethics charter that outlines the principles followed in prompt engineering

• Defining a content safety policy that prohibits harmful or offensive outputs

Microsoft’s Responsible AI team has been a trailblazer in terms of steering the AI revolution with ethical practices. The following figure published by Microsoft can serve as a guide to structuring safety metaprompts, focusing on four core elements: response grounding, tone, safety , and jailbreaks. This approach is instrumental in implementing a robust safety system within the application layer. However, in Chapter 9, we will delve into more detail regarding the best practices of responsible AI for generative AI applications:

Figure 5.16 – Metaprompt best practices from Microsoft

Summary

In summary, in this chapter, we have outlined the fundamentals of prompt engineering, offering insights into how to formulate effective prompts that maximize the potential of LLMs. Additionally, we have examined prompt engineering from an ethical perspective. Thus far, in this book, we have explored the essential elements and methodologies necessary for constructing a solid generative AI framework. In the next chapter, we will integrate these concepts with application development strategies for generative AI involving agents. We will also discuss methods for operationalizing these strategies through LLMOps, which stands as a critical component in the automation process.

Generative AI application development frameworks

In this section, we will focus on the popular generative AI-based app development frameworks used by developers today in their applications, as they add functionality and extensibility to LLMs.

But why do we need to modernize existing ML applications to use intelligent generative AI in the first place? Let’s compare and contrast application characteristics without generative AI and the modernized applications infused with generative AI.

Current ML applications have some of these common characteristic limitations:

• They are constrained with their interactions, especially with generative AI services.

• They are hard-coded and usually have a fixed dataset. For example, one can leverage certain datasets to train certain ML models, and those models are fixed.

• If they then want to change a model within an application or if they want to change the dataset entirely, they will need to again retrain the model, which is a challenge because of increasedcosts and increased time to completion.

• Retraining the model involves adding enhancements or features, which is quite complex and also time-consuming and costly.

However, with intelligent generative AI applications that use the techniques described in this chapter, you can do the following:

• Use natural language interactions. We have seen this in ChatGPT and other applications, where one can begin chatting as if there is an actual human or assistant. In addition to just using natural language to interact with generative AI applications, you can easily have your own personalized experiences based on human-like characteristics, such as personas and emotional tones, within an interactive session.

• Generate data-driven and personalized experiences tailored to a user or set of users. Additionally, these applications can improve over time, autonomously using past experiences.

• Instead of a longer, time-consuming process of traditional software development, you can quickly deliver new features and product enhancements.

As you can see, intelligent generative AI applications are enabling us to create solutions and address problems never before and at a pace we have also never seen before. Now let’s turn our attention to some modern App Dev frameworks that can help us implement the new and sophisticated features.