Prompt Engineering: A Balance Between Risk and Reward.

Weekly Executive Insight from Shiv Singh, CISSP , CEO of LINEARSTACK

Prompt engineering is the science of crafting questions to best leverage Natural Language Processing (NLP). The goal is to get a well-crafted and usable response from generative AI and machine-learning model tools.

Like any science, prompt engineering has a positive and negative reality, including legal issues, a lack of ethical guidelines, and other risk considerations. Prompt engineers create input requests and receive the output, aligning with their expectations. However, often, with AI, these results contain biased opinions, misleading results, or completely unusable datasets.?

Accurate responses stemming from a prompt require deep understanding and constant fine-turning, including adjusting phrases, content, and overall structure within the original prompt. Engineers developing their skills in prompt science need to have a solid understanding of how AI modeling works.?

Hackers also use prompt engineering to help create their next-generation cyberattacks, which include email phishing, ransomware, prompt injection attacks, and identity theft.?

Prompt engineering is a future of AI consumption for organizations wanting to mine data from large language models (LLM). However, this science also carries high risk.?

Definition of Prompt Engineering.?

Prompt engineer creates queries to execute against ChatGPT-3 or other Open AI LLMs. The idea of the prompt helps organizations gain the most relevant information embedded within AI.

?Prompt engineering (PE) has become essential in organizations heavily invested in AI and ML. PE is critical for organizations wanting to recognize a return on their investment in AI. Without well-crafted prompts, organizations will probably end up with biased outputs or answers with false conclusions.?

Prompt engineers are valued for their ability to produce high-quality output quickly. It's essential to consider the cost of the employee compared to the benefits. For example, if generative AI takes 10 minutes to respond and a prompt engineer can do it in 5 minutes, that saves 50% and is very appealing.

What is the Risk of Prompt Engineering?

Effective, prompt engineering delivers exceptional and usable results for organizations wanting to monetize their data and gain additional benefits by leveraging AI. Ineffective prompt engineering leads organizations down an utterly original path, often resulting in financial implications, lawsuits, and a waste of value in economic capital.?

Here are examples of prompt engineering issues and risks:

Implementation Shortcomings:

Subpar implementations often drive additional costs to maintain their AI investments, including additional human and financial capital. Investing in experienced, prompt engineering may be costly, but the return on investment (ROI) in AI would significantly improve. Leveraging the experience of AI data scientists and architectures will also help with the ROI.

Workflow Challenges:

Without a seasoned, prompt engineer, many AI workflow outputs will cause delays due to their incompleteness. The redundancy of prompts producing different outputs will confuse the organization.?

Skewered Data-Driven Miscalculations:?

Confusing AI outputs will lead to decisions based on AI data, resulting in poor outcomes. Organizations becoming more dependent on AI outputs could jeopardize business operations, financial systems, and product development.

What SafeGuards and Decisions Should CEOs Make Regarding Prompt Engineering?

Numerous organizations are investing significantly in acquiring or developing generative AI systems to improve efficiency, enhance customer experience, and achieve a strong return on investment. CEOs who opt to decrease their investment in prompt engineering and AI may encounter unintended repercussions.

Failure to prompt engineering can lead to lower output quality, which can impact various organizational functions such as report generation, customer interactions, and decision-making.

Prompt engineering may be popular now, but it needs more sustainability and versatility in the long run. Focusing too much on finding the perfect words can hinder problem-solving and creativity. Mastering problem formulation could be crucial for working effectively with advanced AI systems in the future, much like learning to program languages was in the early days of computing.

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Interesting in learning more about prompt engineering? Contact the consultants at Linearstack to learn more! Click here to schedule a discussion with their engineering experts!

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