20 Prompt Engineering Paint Points

Even though it's not clear how many jobs there are for prompt engineers, just about every job will be impacted by prompt-based tools. Personally, I'm intrigued with the idea that using AI tools like ChatGPT and Dall-e 2 could help a person get interested in AI, and maybe inspire them into getting involved in learning more.

GPT-4 had some interesting things to say about pain points that prompt engineers face. Pain points are important because they focus on how a person/product is solving a problem.

There's ongoing discussion of what a prompt engineer actually is, but we could say there is professional and personal prompt engineering. Prompt engineering revolves around the text inputs that are given to tools like ChatGPT or Dall-e2. An "official"/professional prompt engineer generally works at a company that is developing an AI model and helps to refine it, giving it feedback and other areas. This article focuses on the challenges that prompt engineers face and the things they do, based on what GPT-4/ChatGPT produced.

The original query produced a list of 20 results, and they all seem accurate. Learning about them can help you see how every day users, or "personal prompt engineers", are also participating in this process.

I'm going to leave the GPT-4 output unedited, and uncut. At least one of them is out of date, but it will be interesting to discuss why.

Q: What are the main pain points for prompt engineers?

GPT-4: Prompt engineers, also known as AI trainers or AI prompt developers, work with language models like ChatGPT to create useful and effective prompts to elicit desired responses. They face several pain points in their work, which may include:

1. Ambiguity: Crafting clear and unambiguous prompts is challenging. If a prompt is vague or open-ended, the AI may generate responses that are not what the engineer intended.
2. Limited model knowledge: Language models have a knowledge cutoff, and new information or events that occur after this point are not known to the model. This can lead to outdated or incomplete answers.
3. Bias: AI models can inadvertently learn biases from the training data. Prompt engineers must be vigilant in identifying and addressing these biases to ensure fair and unbiased responses.
4. Model unpredictability: AI models may sometimes generate unexpected or off-topic responses, which can be frustrating for prompt engineers seeking specific outcomes.
5. Length and verbosity: Striking the right balance between brevity and comprehensiveness can be difficult. A prompt may need to be concise to avoid overwhelming users, but also detailed enough to guide the AI towards the desired response.
6. Iterative process: Developing effective prompts often requires multiple iterations and refinements, which can be time-consuming.
7. Constantly evolving technology: AI models and their capabilities are continually changing, requiring prompt engineers to stay up-to-date with the latest advancements and techniques.
8. Ethical considerations: Prompt engineers need to be mindful of potential ethical issues surrounding AI-generated content, such as privacy concerns, misinformation, and manipulation.
9. User adaptation: Engineers must consider how users interact with AI systems and tailor their prompts accordingly. This can involve accounting for different levels of user expertise, cultural backgrounds, and language abilities, which can be a complex task.
10. Evaluation and metrics: Establishing objective metrics to evaluate the quality of prompts and the AI's responses can be challenging. Engineers must balance factors such as relevance, coherence, and informativeness when assessing performance.
11. Collaboration and communication: Prompt engineers often work closely with other team members, such as data scientists, developers, and domain experts. Effective communication and collaboration are essential for ensuring that prompt engineering efforts align with project goals and requirements.
12. Scalability: Developing prompts that work well across various use cases, industries, and applications can be difficult. Engineers need to create prompts that are adaptable to different contexts while maintaining their effectiveness.
13. Handling edge cases: It's important for prompt engineers to consider and address edge cases that may lead to inappropriate or harmful AI-generated content. This requires a deep understanding of both the AI model and the potential risks associated with specific prompts.
14. Balancing user control and AI assistance: Prompt engineers need to find the right balance between giving users control over AI-generated content and providing helpful AI-generated suggestions. Striking this balance can be challenging but is crucial for a positive user experience.
15. Training data quality: Ensuring the quality of training data used to fine-tune AI models is critical, as it directly impacts the model's performance and the effectiveness of the prompts. Prompt engineers must collaborate with data teams to ensure that the training data is accurate, diverse, and representative of the target user population.
16. Model understanding: Prompt engineers need a deep understanding of the AI model's inner workings, strengths, and weaknesses in order to develop effective prompts. This requires ongoing learning and staying up-to-date with the latest research and advancements in AI.
17. User feedback integration: To improve the effectiveness of prompts and AI-generated responses, engineers must collect and integrate user feedback into the development process. This involves identifying patterns in user behavior, addressing user needs, and refining prompts accordingly.
18. Maintaining user trust: Prompt engineers must ensure that AI-generated content is reliable, accurate, and transparent in order to build and maintain user trust. This includes addressing potential biases, addressing privacy concerns, and providing clear information about AI-generated content's limitations.
19. Intellectual property and legal considerations: Engineers must be aware of intellectual property, copyright, and other legal considerations when developing prompts and AI-generated content. They must ensure that the AI system adheres to relevant laws and regulations.
20. Balancing innovation and risk: Prompt engineers need to balance the drive for innovation with the potential risks associated with AI-generated content.

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I plan to write more in detail about each pain point/challenge, and future articles will be available via this linkedin account and on https://learnskills.tech, an informal site I am working on with students. -Todd