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A Complete Guide to Master AI Prompt Engineering and Perfectly Train Your AI Model

You get what you ASK for, especially if you are asking large language models like ChatGPT or Gemini (previously known as Bard). They give confusing or incorrect outputs that do not always match with users’ expectations. Well, the fault lies in questions. In this AI prompt engineering guide, we will tell you what prompt engineering is, how to write effective prompts, different types of prompt engineering, and the best practices to follow.

A Complete Guide to Master AI Prompt Engineering and Perfectly Train Your AI Model

What is AI Prompt Engineering?

Any statement, command, or question that makes the AI model to act or respond is known as prompt. The art of writing an effective prompt to get a specific or desired outcome from the model is prompt engineering.

Writing Prompt Isn’t Enough, Writing Good Prompt Is!

Think of AI models as a human who is given a new task about which he/she has less experience or knowledge. To do that task effectively, he would need clarifications on the task, how to begin with, steps to follow, and more.

The same goes with an AI model. It also needs clarity on the action, background information related to it, specific things it needs to consider when producing outputs, and an output indicator to tell how the output should be i.e. style and tone. A good prompt has all these elements, and they make a difference in the output.

Here’s how.

Scenario: An employee wants information about the company’s leave policies

Good Prompt: “Hey, I want to know how many casual leaves are entitled to me as per my company’s leave policy for the year 2023-2024.”

Bad Prompt: “Tell me about the casual leaves.”

In both cases, AI would respond but the user will get the desired output from prompt 1 as it is clear, specific, and action oriented. Whereas the second prompt is vague and generic.  

6 AI Prompt Engineering Techniques to Write Effective Prompts for Your AI Model

There is not one way to write good prompts for large language models. Different types of prompt engineering techniques help in getting the desired results.

1. Zero-shot Prompting: Only instruction, no example.

For example, ask the large language model to “Write a blog on prompt engineering.” Here no word count is mentioned so the LLM will produce a blog of any word count and may not give specific information you want to add. This technique is good when you want quick results without any specific details.

2. One-Shot Prompting: Instruction with a single example.

Giving one example helps the AI model in understanding the kind of response the user is expecting. An example of one-shot prompting is “Write a blog description like this: This blog talks about the different types of prompt engineering that help in writing good prompts.” You can also add style and tone references for better outcomes. However, make sure that the example is relevant to the desired output.

3. Few-shot prompting: Instruction with more examples

If you want more specific results, then use the few-shot prompt engineering technique. It is the same as a one-shot, only difference is more than one examples are given to the AI model. However, examples should have similar context otherwise results will be vague.

4. Chain-of-thought prompting: Sequence of related prompts

A series of prompts or questions are given to the LLM when you want the response logical. It starts with an initial prompt to set a context for the model followed by a new prompt based on the previous response. These two prompts help the model to understand the context and maintain continuity in the response. Refine the final response with a feedback prompt for the previous responses.

This is one of the best types of prompt engineering. It is useful for learning about new topics, problem-solving, decision-making, content generation, and research.

5. Iterative prompting: Prompts based on the responses

Undoubtedly, the first response won’t be the best. You must train an AI model to fine-tune the output and this technique is called iterative prompting. Here, you refine the next prompt based on the previous response. This way you tell AI how exactly the response should be. However, it takes more time and effort to get the desired results.

6. Negative prompting: Tell what not to do

Instead of guiding the AI model on what to do, tell it what not to do. This way the model will know things to avoid while producing the output. The result would be more specific, and relevant, and exclude everything you don’t want in the output. However, balance is key as too many restrictions will limit the creativity of the LLM. Also, be specific in the instructions and keywords to avoid any misinterpretation by the mode

7. Role-play prompting: Assign a role

Instead of giving so many examples or multiple prompts to the AI model, give him a role to play. For example, ask him to be a data scientist who needs to gather and analyse data on customer behaviour for an e-commerce company. Tell the initial steps for data collection. The model will give step-by-step instructions.  

Role-playing is one of the best types of prompt engineering techniques as it gives clarity on the user and actions to be performed.

8. Model guiding prompting: Let the AI model ask questions

This prompt engineering technique reduces all the guesswork and multiple prompts you have to give to the AI model. Simply, tell the model what you want it to do and ask for details or information it requires for completing the task.

These 8 techniques of AI prompt engineering are the best practices to get fine-tuned responses from the LLMs.

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What is the Future of AI Prompt Engineering?

Prompt engineers would have half of the world's jobs shortly with the rise in AI models in different industries. Businesses would look for engineers who can speak the AI language and make them understand the human language to perform specific tasks. This would require prompt engineers to understand how the model works, responds, breaks down the complex tasks into structural components to guide its response, make it adaptable, and learns from different prompts.

Besides these skills, prompt engineers should stay updated about the future trends in this field.

Here are some potential trends:

  • Fine-Tuning Techniques: Further refinement of fine-tuning methods for specific tasks and domains could lead to more precise and context-aware responses. This involves training models on narrower datasets to make them more specialized and accurate in particular areas.
  • Multimodal Integration: Integrating prompt engineering with multimodal capabilities, such as incorporating images, videos, or other non-text inputs, could open up new possibilities for diverse applications, from content creation to enhanced communication.
  • Collaborative Prompting: Collaborative prompt engineering, where multiple users can contribute to refining prompts or generating content collaboratively, could emerge. This could facilitate group decision-making processes and creative content creation.

Final Words

As we are moving forward in the future, we cannot ignore the fact that AI will be a part of it. 

From a chatbot for customer support to virtual assistants for personalized recommendations, AI is finding space in our daily lives. Prompt engineering is the bridge between humans and AI through which we can make our intentions clear to large language models and guide them to do specific tasks.

However, it is more than just training AI. Prompt engineering is a promising career for anyone who wants to explore and stretch the capabilities of AI models.

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