The Art of Crafting Powerful Prompts

• 20 June, 2024
• By: Sudhir
• 8 Comments

The Art of Crafting Powerful Prompts

There are some problems to solve in RAPs and PAs. To address these issues, a relatively new field called prompt engineering has emerged, focusing on developing and optimizing prompts to use LLMs (Large Language Models) across different industries and studies. This skill also highlights the strengths and weaknesses of LLMs, thereby enhancing their functionality in areas such as question answering and mathematical computation. In essence, prompt engineering is employed to determine viable and efficient methods of prompting that integrate with researchers, developers, LLMs, and other tools.

LLMs are AI algorithms known for their use of specific techniques and large datasets to accomplish tasks such as comprehension, generation, summarization of new content, and prediction. These models employ autoregressive properties to produce text by calculating the likelihood of the subsequent word in a given sequence from an initial cue.

Prompt engineering involves the preparation of proper and efficient descriptions or instructions to influence a language model or an AI system. This includes developing questions or instructions that are input into the system to obtain the specific response or output required from the AI. Implementing retrieval techniques is vital for fine-tuning large models such as GPT-3, ensuring they produce accurate results. By designing the right kind of prompts, developers can guide and define the nature and appearance of the AI’s responses.

LLMs are capable of generating relevant responses to perform various tasks; however, they face challenges such as lack of practical thinking, poor contextual awareness, difficulty maintaining coherent structure, and misunderstanding the meaning of texts on which they are based. These challenges are addressed through prompt engineering, which involves informing, restraining, or providing further directions to the language model with deliberately constructed prompts.

Key Components and Techniques in Prompt Engineering

Thus, the key components of the artificial intelligence model and the concept of AI should be chosen depending on the goals set and the possibilities of the AI technology. There are many advantages to prompt engineering, such as well-managed output, accuracy, reduced bias, customization for certain models, better context consideration, alignment with the purpose, time-saving, and support for ethical principles in AI utilization.

Various techniques can be applied to improve the output of these models, forming the general idea of prompt engineering. These include:

• Delivering clear instructions

• Defining the required format

• Challenging the randomness of the response by regulating temperature

• Re-evaluation and feedback for more prompt revisions

Specific techniques include:

• Zero-shot prompting: No example is given, and the LLM operates under this prompt.

• Few-shot prompting: Examples are provided to help the model respond when zero-shot prompts do not work.

• Chain of thought (CoT): Tasks are broken down with reasoning expertise.

Let’s illustrate the specificity of prompt engineering by using the concept of developing an order bot based on the OpenAI API. This entails creating functions to use the API, providing context help that describes the bot, explaining what it does, and listing the functions that are available, as well as the interface through which the user interacts with the bot. The context prompt is vital because it defines the scope of the responses that the bot will make, particularly concerning the order-taking process of the business or organization in question.

Application of Order Bots in Prompt Engineering

I will explain how and where the usage of an order bot applies to concepts in prompt engineering, particularly in practical applications. This allows developers to encompass tasks such as greeting customers, taking orders, and accepting payments, all while maintaining the appropriate linguistic tone.

In prompt engineering, cue creation for language models relies on the ability to design effective prompts. Prompts directly influence the text content and its parameters, shaping the choice of wording, tone, and purpose. Well-crafted prompts enhance the general performance and efficiency of language models for users. Experience and practice are key, as one must also be willing to learn and continuously improve in prompt engineering.

Indeed, prompt engineering represents the future of AI technology. As AI becomes increasingly integrated into consumers' lives, there is a growing demand for prompt engineers who can make interactions with AI systems more efficient. While coding skills are beneficial, prompt engineering primarily revolves around language and AI model communication, making it accessible to those with strong language skills and knowledge of AI capabilities.

For those interested in learning more about prompt engineering, there are various educational resources available, including courses, articles, and guides. Related topics in prompt engineering include definitions and real-world examples, basic and advanced guidelines, and examples of prompt engineering in models like GPT-3 and other AI systems.

The Growing Significance of Prompt Engineering

With the advent of prompt engineering, a huge scope of opportunities has opened up for professionals and enthusiasts alike. It provides a roadmap to what is most likely an extremely rewarding professional career, where prompt engineers are crucial in developing and optimizing AI applications across a wide range of industries.

To excel in prompt engineering, one needs to understand the basic tenets of this field, especially their application in generative AI. This includes knowledge of concepts such as zero-shot and few-shot learning, understanding the relationship between context and prompts, and knowing how to construct prompts for various task types.

Practical experience in prompt engineering is essential, whether working with different AI models or multiple applications. It also involves continuous fine-tuning of prompting techniques to achieve the best outcomes based on results and feedback. Additionally, staying updated on innovations in AI and language models is crucial, as these developments often impact the practices of prompt engineering.

The area of prompt engineering intersects significantly with ethics. Given the power to shape AI output, engineers bear the responsibility to consider the potential effects of their prompts and to ensure their designs promote ethical and responsible AI usage. This includes being vigilant about bias, preventing unsafe content, and ensuring that AI is applied for its intended purposes.

Prompt engineering is a rapidly evolving and significant area of research in artificial intelligence today. It serves as the bridge between human intent and machine output, making AI more effective and finely tuned. As AI continues to integrate into various aspects of our lives, prompt engineering skills will be of prime importance in shaping the future of human-AI interaction.

Creating an Order Bot

Using the prompt engineering technique, you can create an order bot with OpenAI’s API.

                                import openai
                                import os
                                from dotenv import load_dotenv

                                load_dotenv()
                                openai.api_key = os.getenv("OPENAI_API_KEY")

                                def get_completion(prompt, model="gpt-3.5-turbo"):
                                    response = openai.ChatCompletion.create(
                                        model=model,
                                        messages=[{'role': 'user', 'content': prompt}],
                                        temperature=0
                                    )
                                    return response.choices[0].message["content"]
                                

Providing Prompt as Context

                                import panel as pn  # GUI
                                pn.extension()

                                panels = []  # collect display

                                context = [{'role': 'system', 'content': """
                                You are OrderBot, an automated service to collect orders for a pizza restaurant.
                                You first greet the customer, then collect the order,
                                and then ask if it's a pickup or delivery.
                                You wait to collect the entire order, then summarize it and check for a final
                                time if the customer wants to add anything else.
                                If it's a delivery, you ask for an address.
                                Finally, you collect the payment.
                                Make sure to clarify all options, extras, and sizes to uniquely
                                identify the item from the menu.
                                The menu includes:
                                pepperoni pizza  12.95, 10.00, 7.00
                                cheese pizza   10.95, 9.25, 6.50
                                eggplant pizza   11.95, 9.75, 6.75
                                fries 4.50, 3.50
                                greek salad 7.25
                                Toppings:
                                extra cheese 2.00,
                                mushrooms 1.50
                                sausage 3.00
                                Canadian bacon 3.50
                                AI sauce 1.50
                                peppers 1.00
                                Drinks:
                                coke 3.00, 2.00, 1.00
                                sprite 3.00, 2.00, 1.00
                                bottled water 5.00
                                """}]

                                def collect_messages(_):
                                    prompt = inp.value
                                    inp.value = ''
                                    context.append({'role':'user', 'content':prompt})
                                    response = get_completion(prompt)
                                    context.append({'role':'assistant', 'content':response})
                                    panels.append(
                                        pn.Row('User:', pn.pane.Markdown(prompt, width=600)))
                                    panels.append(
                                        pn.Row('Assistant:', pn.pane.Markdown(response, width=600,
                                        style={'background-color': '#F6F6F6'})))

                                    return pn.Column(*panels)

                                inp = pn.widgets.TextInput(value="Hi", placeholder='Enter text here…')
                                button_conversation = pn.widgets.Button(name="Chat!")

                                interactive_conversation = pn.bind(collect_messages, button_conversation)

                                dashboard = pn.Column(
                                    inp,
                                    pn.Row(button_conversation),
                                    pn.panel(interactive_conversation, loading_indicator=True, height=300),
                                )
                                dashboard
                                

Output

Creating an Order Bot using OpenAI's API demonstrates the power of prompt engineering to create useful applications.