Prompt engineering, LLM, and Supervised Learning

The 9 Critical Responsibilities of an AI Prompt Engineer

Prompt engineering, LLM, and Supervised Learning
LLMs require supervised learning and prompt engineering

Prompt engineer is brand new role and career opportunity that did not exist until a few years ago until AI started to gain traction in the technology landscape.

Every time there is a technology evolution, new type of jobs are created that would not have existed before.

So what is Prompt Engineering, and why is everyone talking about it?

It is the work involved to direct AI programs known as large language models (LLMs) to perform intricate tasks and produce accurate results.

With LLMs like GPT 3, GPT 3.5, and now GPT 4, prompt engineering is now a large scale career opportunity for companies looking to adopt these models in their AI use cases.

Here are the 9 critical responsibilities for a Prompt Engineering job:

1. Task Understanding: The prompt engineer must have a clear understanding of the task or application for which the model will be used. They should design prompts that are relevant and in line with the task at hand.

2. AI Model Understanding: The prompt engineer should have a good understanding of the AI model’s abilities and limitations, and should design prompts that are within these capabilities.

3. Creativity: The prompt engineer should be able to craft creative and varied prompts that motivate the model to generate unique and diverse outputs.

4. Clarity and Precision: The prompts should be clear and precise, making it easy for the model to comprehend the task and remain focused.

5. Quality Evaluation and Testing: The prompt engineer should be able to test and evaluate the model’s output and use the results to enhance the prompts and improve the model’s performance.
Continuous Improvement: The prompt engineer should continuously monitor and upgrade the performance of the prompts and adjust them as needed.

6. Data Bias Awareness: The prompt engineer should be mindful of potential biases in the training data and design prompts that minimize these biases.

7. Collaborative Approach: The prompt engineer should work effectively with other team members such as data scientists, engineers, and product managers.

8. Technical Expertise: The prompt engineer should have a good understanding of machine learning, natural language processing, and related technologies and possess programming skills.

9. Domain Knowledge and Industry Awareness: The prompt engineer should stay updated with the latest developments in the field and be able to apply that knowledge to their work.

The role of a Prompt Engineer is crucial in the development and deployment of AL LLMs (large language models).

What are your thoughts on this new role?

#futureofwork #ai #aijobs #machinelearning #digitaltransformation

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