Human-in-the-Loop and Adaptive Prompting Are the Future of AI Development

AI doesn’t always produce optimal outputs. We’re seeing it all the time. And contrary to popular belief, it’s not because the AI system is flawed. The issue is that AI is now accessible to everyone and ChatGPT alone has 180 million active users. Create a ChatGPT account, and you can access a system that will pretty much tell you anything you need it to…but it doesn’t always make sense.

Creating the desired outputs is a science and an art. Dzone writer and developer evangelist Pavan Belagatti provided an insightful overview of prompt engineering in this article. As he points out, there are multiple approaches to prompt engineering. 

Adaptive prompting and human in the loop prompting are two of the most effective approaches. Huixue Zhou and his colleagues at the Huixue Zhou and his colleagues at the Institute for Health Informatics at the University of Minnesota published an article showing that adaptive prompting can be particularly useful for biomedical relation extraction and can even have F1 scores of 95.13. There are many other great applications for adaptive prompting. 

What Are the Top Prompt Engineering Techniques?

Matthew McMullen wrote another article on Dzone detailing how prompt engineering is the key to mastering AI. One of the best ways to create ideal outputs is to focus on adaptive prompting. We will provide some insights into it below. But first, we will identify the leading prompt engineering techniques and then focus on how adaptive prompting fits with them.

Think of AI as a toddler. Sometimes, you need to explain things a little differently to get the optimum outcome. And if you give it the right prompts, it’ll give you the best output.

There are different techniques that you can use to get the right outputs. They include the following:

  • Zero-Shot Prompting. Zero-Shot Prompting involves making a single prompt without any additional context to get the right output. 
  • One-Shot Prompting. One-Shot Prompting is similar to zero-shot programming, except that it entails using an example of a desired output to yield the best result. 
  • Chain-Of-Thought Prompting. This approach to prompting involves breaking the process down into multiple steps and using one or more prompts to complete each.
  • Human-In-The-Loop. Human-In-The-Loop involves the prompt engineer providing feedback throughout the process to fine-tune the outputs. 

All of these techniques have their own benefits and purposes. 

It’s interesting to see how you can influence AI and get optimum search results with the new phenomenon that’s prompt engineering. In fact, we’d say that if you don’t understand prompt engineering, there’s not much point in using AI. It became such a necessity that you can even land a career in prompt engineering if you know how to do it well enough. The funny thing is, that all prompt engineering does is find the best way to instruct AI to get the output you want. How is it done right? 

Read on to learn more.

Why Prompt Engineering is Essential

The question should be, why wouldn’t prompt engineering be essential? If you’ve used AI, you’ll know that it doesn’t always tell you specifically what you need to know the first time. Here’s a basic example:

First prompt: Can you tell me how to quickly save money?

Engineered prompt: Can you tell me how to save $1,000 in 6 months when my monthly income is $1,200 and my expenses are $500?

It’s about adding detail, depth, and relevance to the question to get the optimum output. And it’s more than simply getting the best answers. Prompt engineering can help mitigate bias and enhance the overall user experience.

But it’s not all smooth sailing. Something known as prompt injection is a security vulnerability that affects AI models when an attacker aims to reveal unintended responses from AI tools.     . Follow the link shared to learn more.

Adaptive Prompting

Adaptive prompting is one of the most exciting trends. It’s an incredible way of fine-tuning AI to get a response that suits each user. It’s the constant analysis of user feedback and preferences, allowing AI to keep improving to better “understand” user needs.

Adaptive prompting is pretty much like the example given      above. It should transcend a static prompt approach and create a dialogue of learning to enhance AI’s understanding of exactly what we’re asking.

Take another look at the example above. A static command is asking AI to help you save money. An adaptive prompt adapts to your specific needs – you’re telling the AI how much money you have coming in, your expenses, and the timeframe of when you need to save the money. You’ve adapted the prompt to your specific needs and enhanced the AI systems understanding.

It’s one of the most simple AI prompt engineering trends that anybody can use.


One of the criticisms of AI is that, sometimes, if you rely too heavily on it without human insight and oversight, it goes down a wayward path that makes no sense. Despite its future aspirations, AI is still nowhere near human understanding and cognitive ability, it just knows a lot of information and can spew it out to you if you ask it. And, even then, it doesn’t always do it right.

The human-in-the-loop approach recognizes Click here to enter text. the benefit of human oversight. Prompt engineers use the human-in-the-loop approach to ensure the responses meet human expectations. This is done in several ways:

  • Feedback: AI-generated responses enter a human feedback loop. A prompt engineer can tell AI, ‘No, you answered that wrong. You should have focused on this.’ The result is an enhanced AI model performance.
  • Adaptability: AI systems constantly need to adapt to new information, trends, inputs, etc. Human oversight ensures AI systems are kept relevant and responsive.
  • Quality Control: As we said, AI isn’t at the point of human understanding and reasoning; it just has a lot of data. Quality control ensures relevance, accuracy, and improved outputs.

Domain-Focused Prompt Engineering

Domain-focused prompt engineering is a growing trend because AI is general, at least until you make it focused. Domain-focused prompt engineering makes sure responses are tailored to specific industries using specific language and terminology related to that field. For example:

First prompt: Can you tell me about how SEO can help my business rank?

Domain-focused prompt: Can you tell me about SEO in the context of ranking in the SERPs and the trending keywords I should be focusing on for selling sunglasses?

It’s specific, domain-focused, and guaranteed to give more accurate results. If anything, we’d say it’s similar to adaptive prompting, except it focuses on specific industries and domains.

Focusing on prompt engineering will help you get the most out of AI platforms. And if you don’t know how to do it, apparently you can hire an AI prompt engineer to do it for you.

The post Human-in-the-Loop and Adaptive Prompting Are the Future of AI Development appeared first on Datafloq.

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