The best prompt engineering lecture that delves into the fundamental concepts of LLM.

When we talk about prompt engineering, we often think of it as "how to ask questions to AI," but more accurately, it's "the work of constructing high-quality prompts to get high-quality results from AI."
If we want the prompts we write to go beyond simply typing questions into a search portal, it is essential to understand how LLM works and systematically construct prompts.


This book presents the vast content of prompt engineering in a compact and well-structured manner that is easy to understand and access even for those without programming knowledge.
We've made it possible for anyone to systematically learn prompt engineering, which is essential common sense for living in the age of artificial intelligence, from basic concepts to practical training.
We've restructured Fast Campus' popular lectures into a format that even beginners to LLM can easily learn, and carefully selected practical content focused on practical application, such as refining YouTube/blog titles, creating English vocabulary lists, and generating job interview questions.

Most of the ChatGPT usages on the market are what are called blind prompting or prompt writing, not prompt engineering.
Because there is no design to evaluate ChatGPT's responses, we cannot guarantee the consistency and accuracy of the results.
This book explains how to achieve accurate and consistent results as intended, based on experimental methodology, rather than just randomly producing plausible results.
By experimenting with various prompts, analyzing the results, and continuously improving, we aim to control AI as intended and maximize the user experience.
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This book teaches you how to 'write' prompt templates, not how to 'use' them, and how to construct programs from scratch with prompts.
Of course, if you can code, developing LLM applications using libraries can be much easier.
However, the reason why we learn from the basics like this is that using external libraries or prompt templates has low extensibility and it is very difficult to debug (identify and resolve problems) when there are problems.
Even the most widely known libraries will continue to evolve, and new technologies will constantly emerge.
Therefore, developing the ability to create and structure your own prompts will have far greater consequences than simply leveraging AI.
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We investigated what changes occurred in CS agents after the introduction of AI using LLM, and found that the number of consultations resolved per hour increased by 13.8%.
This means that the speed of processing a single consultation has increased, while the number of consultations processed per day has also increased.
Another major factor contributing to the change is the increased rate of successfully resolved consultations.
But the key point here isn't the increase in the number of cases resolved. While it typically takes about six months of training for a CS agent to develop high-level consulting skills, the key point is that with AI, they can achieve the same level of skill in just two months.
This has resulted in significant changes across the company, including improved customer satisfaction as well as agent satisfaction, and a significant increase in agent tenure as well as work processes.
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In this era of great transformation, management cannot help but feel both opportunity and crisis.
A technology that was worth hundreds of billions of dollars just a few days ago has suddenly become extremely cheap and accessible to anyone.
Anyone can now create advanced AI applications that were once thought to be only possible by Google, Microsoft, or Naver in Korea.
Failure to keep up with this pace can leave management feeling uneasy, as they risk falling behind.
Although times are changing like this, it is difficult to get a good feel for it at the practical level.
Anyone can easily follow along, so the question is why do we have to do it?
However, there is a saying that “AI is not going to take your job, the person who uses AI will take your job.”
It's something you should do right now because it's easy for anyone to follow.
This requires a shift in mindset that discards existing methods.

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