Prompt Engineering: How AI Whisperers Are Shaping the Future of Work

Discover how prompt engineering is transforming the job market and why mastering this skill can future-proof your career in artificial intelligence

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This post can be read in Tocsin Magazine and all Medium members can read the full article here.

Introduction

If you have ever wondered which jobs will be in demand in the future, this is one of them — it has already arrived and is highly sought after: the job of a “Prompt Engineer.”

Simply visit any job listing website, and you will find numerous large companies, as well as small firms with just a few employees, searching for Prompt Engineers.

But what exactly is prompt engineering, and what do prompt engineers do?

What Is Prompt Engineering?

In the simplest terms, prompt engineers are experts who specialize in creating text-based prompts that interact with artificial intelligence (AI) models such as ChatGPT or Gemini. They also test and optimize these prompts to improve AI responses.

They are often referred to as “AI Whisperers” because of the unique nature of their work. Their role in AI development is crucial, as the quality of their prompts directly impacts the accuracy and usefulness of the results generated by AI.

The term “AI Whisperers” comes from the metaphor of an expert “whispering” to artificial intelligence to achieve the desired output, similar to how animal whisperers communicate with animals.

The History of Prompt Engineering

This profession emerged with the rise of artificial intelligence. Before AI, the concept of a Prompt Engineer did not exist.

With the development of digital tools such as ChatGPT, the need arose for specialized professionals who could “communicate” with AI — effectively speaking the language of artificial intelligence.

As AI models became more sophisticated, prompt engineers evolved alongside them, refining their skills to become key figures in AI development.

Today’s prompt engineers are no longer just programmers. The role is not limited to software developers; it also requires an understanding of logic, creativity, and language comprehension. This allows professionals from diverse backgrounds to pursue a career in prompt engineering.

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What Does a Prompt Engineer Do? Responsibilities and Tasks

The primary role of a prompt engineer is to create effective prompts, but their work doesn’t stop there. A skilled prompt engineer must master several key tasks to ensure AI models function optimally.

Key Responsibilities of a Prompt Engineer:

• Creating and Formulating Prompts: They design clear instructions for AI models to produce specific results and constantly experiment to find the approach that delivers the best outcomes.
• Analyzing Results: They evaluate the output, identify any errors or “hallucinations” (inaccurate or misleading responses), and adjust the prompts to improve quality.
• Customizing for Specific Needs: Depending on the industry — whether it’s marketing, medicine, finance, or technical documentation — prompt engineers to tailor the communication with AI accordingly.
• Keeping Up with Trends: Given the rapid pace of AI advancements, they must stay informed about new algorithms, techniques, and breakthroughs to ensure high-quality performance.
• Educating Colleagues: A reliable prompt engineer is always ready to share new insights and best practices with coworkers, helping maintain consistent quality across the organization.

Salary and Job Demand

The more developed and technologically advanced a country is, the more AI is integrated into business operations — driving demand for prompt engineers. This, in turn, influences salary levels.

The highest salaries for prompt engineers are found in technologically advanced regions such as the United States and Europe, and even in Turkey and parts of Asia.

According to various online sources, the average annual salary for a prompt engineer can range from $100,000 to $300,000, reflecting the high demand and the critical role these professionals play in AI development.

Conclusion

The growth of AI technology will undoubtedly lead to the disappearance of certain traditional jobs, but it will also create new career opportunities in the AI sector.

There will be a growing need for AI specialists. So, if you are already in this field — or think you could become a prompt engineer — the world of AI is open to you, and now is the time to seize the opportunity!

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DeepSeek vs. ChatGPT and Others: The Clash of Titans

Explore the Clash Between DeepSeek and ChatGPT, Learn About Their Features, Strengths, and Weaknesses by Comparing Two Leading AI Models, Discover Which AI is The Best

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This post can be read in Tocsin Magazine and all Non-Medium members can read the full article here.

Introduction

A few days ago, the DeepSeek mania began and swept the entire internet. There is no portal or online forum that isn’t filled with headlines about this latest phenomenon. Everyone writes about it, relays news, and provides the latest reports and comparisons, so I concluded that it is necessary to say something about it myself.

But what exactly is DeepSeek, and why all the fuss around it?

What is DeepSeek and Why All the Hype?

DeepSeek is, in fact, the latest form of artificial intelligence coming from China, and I will now explain why there is so much excitement about it.

DeepSeek is a Chinese AI company that builds large open-source language models (LLMs), and its latest model, DeepSeek R1, was released on 10.01.2025 as a free AI model.

What makes this form of AI special and different from the others is the fact that it is trained in both Chinese and English — unlike other models such as ChatGPT, which are primarily trained only in English. This fact makes DeepSeek a truly multilingual AI model that is more adapted to the Chinese than all the others.

Overcoming Hardware Limitations with Smart Engineering

During the development of the DeepSeek model, the Chinese were limited by weaker hardware due to American restrictions on the sale of AI chips. Their smart engineering came into play, demonstrating that with significantly fewer resources and less hardware power, it is possible to develop a model that is just as strong — if not stronger and better — than ChatGPT, which had enormous resources.

According to the Chinese, approximately $6 million was spent on the development of DeepSeek, while the development of Western models such as ChatGPT has cost hundreds of millions of dollars.

It appears that this time the Chinese caught the Americans and Europeans with their pants down, completely unprepared for such a cheap yet highly efficient technology, because no one could have expected it — much less what followed in the heart of the AI hardware giants!

AI Market Shockwaves: DeepSeek’s Impact on Tech Giants

Due to the extraordinary popularity of DeepSeek, which has surpassed the number of downloads of ChatGPT, these events have caused tremors in the USA and European markets.

For example, Nvidia’s stock value fell by 17%, and Broadcom dropped by 17.4%. Other tech giants also sank like the Titanic, with Microsoft falling by 2.14% and Google’s parent company, Alphabet, by over 4%.

In Europe, the Dutch chip equipment manufacturer ASML saw its share price drop by more than 7%, while the shares of Siemens Energy, which also produces AI hardware, fell by 20%.

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DeepSeek vs. ChatGPT: Analysis and Comparison

Now we will analyze both models and try to determine which one is better in which area, highlighting all their advantages and disadvantages.

DeepSeek Advantages

• Free & Open Source Model: Freely available and encourages community contributions.
• Multilingual Training: Specifically benefits Chinese users.
• Specialized Artificial General Intelligence (AGI): More precise and specialized responses.
• Mixture-of-Experts (MoE) Approach: Works like having a team of specialized experts, where only the most relevant ones are called upon for each task.
• 90% Accuracy in Mathematical Tasks: Exceptionally high precision in calculations.
• Superior Technical Writing: Better at drafting professional documents, technical writings, project documentation, and technical specifications.
• Advanced Coding Capabilities: Excels in programming and technical tasks.
• Lower Resource Consumption: Requires significantly fewer resources for operation.
• Low Development Cost: Much lower cost compared to similar Western models.

DeepSeek Disadvantages

• Requires Additional Verification for Complex Answers: May need extra checks on intricate responses.
• Less Intuitive Interface: Not as user-friendly for occasional users.
• Stricter Content Moderation Rules: More rigid in content filtering.
• Potential for Chinese Espionage and Non-Compliance with Western Privacy Standards: Raises concerns about data security and privacy alignment with Western norms.

ChatGPT Advantages

• Well-Known Brand: Recognized and widely used by a large audience.
• Supports Many Languages: A broad multilingual capability.
• Major Partnerships: Collaborations with large firms like Microsoft.
• Large, Active Developer Community: Continuous improvements and contributions.
• General Conversational AI: Excellent for everyday dialogue and explanations.
• User-Friendly Explanations: Simplifies complex topics for non-expert users.
• Better Integration with Multimodal Capabilities: Supports features like voice and image processing.
• Intuitive User Interface: Designed for ease of use.
• Consistent Performance: Reliable across various tasks.

ChatGPT Disadvantages

• Potentially Slower for Technical Computations: May lag in processing complex calculations.
• Limited Number of Free Messages: Restrictions on free usage.
• Higher Computational Costs: More resource-intensive.
• Enormous Development Costs: Requires a significant investment to develop.
• High Premium Prices: Advanced features come with a higher cost.

Conclusion

In summary, we have presented all the advantages and disadvantages we have observed for these two AI LLM models. It is up to you to decide which one is better for your needs and what you will use it for.

When choosing, consider what you actually want from these models and the specific purposes for which you will use them. If you are focused on more technical and specialized tasks, as well as mathematical precision, perhaps DeepSeek is the better model for you. However, if you prefer a user-friendly interface, strong privacy settings, and a well-established brand that has proven itself in the field of AI, then ChatGPT is likely your best choice.

Whatever you choose, keep in mind that both models have made significant advances in their respective fields and together represent a step forward in the realm of AI.

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https://cryptonftworlds.blogspot.com/2025/01/ai-hallucinations-how-artificial.html

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AI Hallucinations: How Artificial Intelligence Errors Can Ruin Careers and Lives

Discover Shocking Real-Life Examples of AI Mistakes, Their Devastating Impact, and How to Safeguard Yourself from Artificial Intelligence Failures

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This post can be read in Tocsin Magazine and all Medium members can read the full article here.

Introduction

“Ah, not Monday again!” I thought to myself this morning as I woke up. Like many others, I’m not the biggest fan of the first day of the week.

But life is what it is, and we have to make the best of it. Mondays can also be a time for new opportunities and beginnings. And as you know, Mondays are a great day to discuss artificial intelligence (AI).

Have you ever written a prompt and received an answer from ChatGPT or Google Gemini that was completely off-topic, false, or unexpected? If so, you’ve likely encountered an AI “hallucination.”

AI hallucinations occur when the system, instead of retrieving accurate and relevant information, fabricates facts that may appear real and plausible at first glance.

For instance, imagine asking AI to list Real Madrid players. Instead of simply naming the starting lineup, it might invent five additional names — individuals who don’t even play soccer, let alone have any connection to Real Madrid.

At first, this might seem like an amusing or harmless error, but the reality is far more serious. A small mistake can disrupt someone’s life, damage their career, or cause irreparable harm and public embarrassment.

Real-Life Examples of AI Hallucinations Gone Wrong

The Lawyer Who Trusted ChatGPT

One of the most notable cases involves attorney Steven A. Schwartz, who relied on ChatGPT for legal research.

The AI-generated fictitious case precedents that did not exist. When the judge couldn’t verify these cases, it became clear that they were fabricated.

As a result, Schwartz and his law firm were fined $5,000. Beyond the monetary penalty, the incident likely caused public embarrassment and a loss of trust in both the lawyer and his firm. This serves as a cautionary tale: double-check everything when using AI for critical tasks.

Google’s AI Bard’s Astronomical Error

During its first public demonstration, Google’s AI program Bard claimed that the James Webb Space Telescope had captured the first images of planets outside our solar system.

In reality, the first such photograph had been taken 16 years before JWST was launched.

This mistake had major consequences. Once the error was discovered, Google’s stock price dropped 7.7% — equivalent to a $100 billion loss in market value — in the following trading day.

Microsoft’s Travel Article Mishap

When a Microsoft travel article published on Microsoft Start mistakenly listed a food bank in Ottawa as a tourist destination — describing it as a “hot tourist spot” and encouraging readers to visit “on an empty stomach” — questions arose about the use of artificial intelligence in generating such content.

While the article contained minor errors in details and locations, the most glaring mistake was labeling a food bank as a prime tourist attraction, a misstep that was quickly noticed and criticized by readers in the comments.

This incident led to the dismissal of 50 journalists due to the reliance on generative AI for Microsoft News articles. It also caused public embarrassment and weakened trust in Microsoft as a reliable source of information.

 

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Why AI Hallucinations Are a Problem

As these examples demonstrate, AI systems are not infallible.

Hallucinations can lead to severe consequences when the information generated is taken at face value without proper verification.

While earlier AI models were more prone to hallucinations, even newer models are not entirely immune. However, as technology evolves, these errors are becoming less frequent. AI engineers are continuously working to minimize such issues, and we can expect better, more reliable models in the future.

How to Safely Use AI Tools

Until AI becomes more reliable, it’s crucial not to place blind trust in it — especially for tasks of critical importance in areas like work, education, health, or legal matters. Always double-check the facts and cross-reference sources.

Remember, AI is just a tool. While it can assist with many tasks, it’s not yet capable of fully replacing human judgment. Proper usage involves verifying its output and supplementing it with human expertise.

That’s all for today! If you have any questions, disagree with any of the points made, or want to share an example of AI hallucinations you’ve encountered, feel free to do so in the comments below.

Let’s learn and grow together in this fascinating era of artificial intelligence!

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If you want to support my work buy me a coffee for $1

https://ko-fi.com/neo77

Greetings and until my next article!

Neo77