Dive into Prompt Engineering: Challenges and Frameworks

The rise of AI models has had a far-reaching impact on the world — all of a sudden, prompt engineers have become the most sought-after and high-paying jobs. As time goes by, prompt engineering will probably become a necessary skill for all job applicants.

For many, prompt engineering seems like rocket science. If you’re the one who interacts with AI chatbots like this: “Hi ChatGPT, suppose you’re a brilliant developer good at …” It’s time to change, as the quality of prompts LLMs receive would largely affect the outputs.

In this article, we’ll explore what prompt engineering is, discuss the challenges it presents, and look at how some effective frameworks can help. I hope that you’ll enjoy the read.

What is Prompt Engineering?

Simply put, prompt engineering is about talking to AI models in the way they prefer. This involves structuring your inputs to help them interpret and respond to natural language effectively. Yes, you got it right—prompt engineering is to break language barriers between AI models and human beings!?

Honestly, creating effective prompts can be quite challenging. Although you may find an official guide to prompt engineering with a specific AI model, just like this one published by OpenAI - Best practices for prompt engineering with the OpenAI API, following the best practices is simply not enough.

If an AI model like ChatGPT always fails to provide the answer you want, learning prompt engineering can make a difference!

The Rise of Prompt Engineering

If that’s not convincing enough, take a look at some stats here.

Immediately after the advent of ChatGPT in 2023, the global prompt engineering market size hit USD 222.1 million, and it was expected to grow at a CAGR of 32.8%. By 2030, the prompt engineering market will be valued at USD 2.06 billion.?

The driving forces of the prompt engineering market include advancements in generative AI, natural language processing (NLP), and the increased demand for more effective interactions with AI models.

I believe that entrepreneurs and C-level executives like me are now actively promoting the use of AI in an attempt to improve overall work efficiency, especially for software development, content marketing, and customer service teams.

According to a recent survey, over 45% of respondents believe that generative AI and prompt engineering will require the most AI skills in the near future.

4 Huge Challenges in Prompt Engineering

When approaching prompt engineering, you may encounter great difficulties.

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1. Poorly Structured Prompts

Believe it or not, writing prompts is not as easy as ABC for an average user.?

Just ask yourself this question, “how many times have you forgotten to check the spelling, grammar, punctuation, and syntax of your prompts?”

Unlike programming languages with strict syntax rules, natural language is flexible and open to interpretation. This flexibility makes it difficult for non-experts to create prompts that consistently yield accurate results.

Indeed, ambiguity can lead to a disaster. Just picture this: by firing up LLMs with “poorly structured prompts,” you are sunk in a swamp of unwanted information.

2. Trial and Error

Prompt engineering is more of an empirical process that you can’t do without trial and error. Scientists all agree that AI is still in its infancy.

You may also notice this, and normally, you don’t make a fuss when given a less-than-ideal answer. Instead, you either change the wording or provide further instructions so that the chatbot can produce a revised version.

That said, you might not recognize the benefits of prompt engineering, especially if you’re not a perfectionist and you’re already satisfied with the efficiency of the AI content generation.?

Chances are, in your eyes, prompt engineering is more time-consuming because you need to experiment with multiple variations of a prompt in order to find out which one works the best for the time being.

3. Unlikely to Be Reused

Not two single tasks are alike in a typical day. If it is not for a complicated task, you won’t be willing to spare time to dive into prompt engineering. This is the case for most people, and I was no exception.

In most cases, prompts are highly task-specific. Unlike people, they are not versatile and, therefore, can’t do everything by themselves.?

Say, you’re a web developer:

• First of all, you need a prompt that can perfectly understand what the product manager wants to achieve;
• Then, another prompt should be able to generate webpages according to UI/UX designs and instructions;
• Next, a new prompt should be able to identify issues, accept the QA engineer’s opinions, and modify the codes accordingly;
• The last prompt should help you launch the webpage and make it accessible to all website users and web crawlers like Googlebot.

To increase the reusability of prompts, you need to be very familiar with your daily routines and split a project into several specific tasks. Honestly, the process itself is overwhelming.?

4. Difficult for Non-Developers

Although it is possible for non-developers to master prompt engineering, with a deep understanding of programming and machine learning, you can learn much faster.?

According to CodeSignal, without prior knowledge of programming, achieving basic proficiency will take you about 2 months.?

An article on GeeksforGeeks reveals the roadmap to prompt engineering, indicating that it’s important to get started with the basics of Natural Language Processing because this is the subfield of AI about the interaction between humans and computers. What’s more, you’ve got to learn Python programming. As you move on, you’ll also need to be equipped to tame pre-trained AI models.

If you think that’s not enough, refer to the syllabuses of top AI degree programs worldwide to design your learning path. As for ambitious undergraduates, consider applying for a master's or PhD degree in Artificial Intelligence, Machine Learning, or Data Science.

Overcoming Challenges: Prompt Engineering Frameworks

To address the challenges above, several structured frameworks have been developed to help users design better prompts. Apply these practical prompt frameworks if you are looking for systematic approaches to simplify prompt creation.

1. RICE Framework

The RICE framework stands for Role, Instructions, Context/Constraints, and Example:

• Role: Define the persona or expertise you want the AI to assume (e.g., a teacher or marketer).
• Instructions: Provide clear directions on what you want the AI to do.
• Context/Constraints: Set any background information or limitations to guide the model’s response.
• Example: Offer examples to clarify your expectations for output style or format.

This framework helps ensure clarity and specificity in prompts while reducing ambiguity.

If you would like some real-world cases, please check out the article here.

2. CREATE Framework

The CREATE framework organizes prompt design into six key components:

• Character: Define the role or persona for the AI.
• Request: Specify what action or task you need.
• Examples: Provide examples of desired outputs.
• Adjustment: Allow room for refining responses based on feedback.
• Type of Output: Indicate how you want the output formatted.
• Extras: Include any additional instructions or constraints.

This approach is particularly useful when creating detailed prompts for more complex tasks. Check out here for some practical use cases of the CREATE framework.

3. LangGPT Framework

LangGPT is a dual-layer prompt design framework inspired by programming languages:

• Prompts are divided into reusable modules (like classes in programming) and internal elements (like functions).
• Modules represent different aspects of a task (e.g., goals), while internal elements provide specific instructions within those modules.

LangGPT aims to make prompt engineering more structured and reusable by providing a standardized format while retaining flexibility for different tasks.

Here’s a paper for you to learn more about LangGPT.

4. RACE Framework

The RACE framework stands for Role, Action, Context, and Expectation:

• Role: Define the character or persona you want the AI to assume (e.g., founder and CEO).
• Action: Specify what action or task you need from the AI.
• Context: Provide background information relevant to the task (e.g., target audience or industry).
• Execution: Describe the outcome you expect from the model’s response (e.g., format or tone).

The RACE framework is particularly effective in marketing scenarios where clarity around roles and outcomes is crucial for generating targeted content.

5. ITAP Framework

The ITAP framework focuses on structured data tasks:

• Input: Define the data the model will interact with.
• Task: Clearly explain what task needs to be performed.
• Annotation: Add labels or tags as needed.
• Prediction: Specify what kind of output you expect from the model.

This framework is ideal for tasks involving structured data processing and machine learning applications.

Conclusion

Prompt engineering is an essential skill for those who want to maximize the capabilities of large language models. The road to proficiency is quite challenging for many, considering ambiguity in natural language instructions and low reusability of prompts, especially for non-developers.

However, with frameworks like RICE, CREATE, LangGPT, RACE, and ITAP, prompt creation can be simplified. What’s more, you can also benefit from improved clarity, reusability, and performance across various tasks.?

Whether you’re an AI expert or an average user, you can craft better prompts and get more accurate and reliable outputs from LLMs by adopting these frameworks.