1. Introduction to Prompt Engineering

Prompt engineering, also informally called prompt design, is
an essential component of artificial intelligence (AI). In a broad sense,
'prompt' refers to the cues that guide AI completion models or APIs to return
expected outputs. At the level of prompt design, thoughtful selection and
configuration of a prompt's content, the presence and magnitude of its
examples, and other accompanying factors support the generation of the intended
completion. When a model is being applied to a language generation task, for
example, an analogously worded prompt might lead to outputs with varying
characteristics, e.g., a kind greeting versus a sarcastic greeting versus a
scornful one. The quality of the prompts used for interacting with AI completes
or for running, evaluations can dramatically affect the perceived and actual
quality of a system. Designing the prompts and selectively providing exemplar
context samples is critical.

Prompt engineering is highly relevant given the recent
development of large-scale pre-trained AI models like generative language
models, and the numerous efforts of humans leveraging AI to meet their business
and leisure demands. Similarly, formidable language generation systems have
been constructed by businesses, academia, and hobbyists, whose model designs,
training data, and revenue structures vary in degrees of freedom. Overall, AI
is increasingly flexing its muscle in the autonomously generated information
and media consumption markets. Being able to influence an AI to generate a
certain output helpful for a specific application is, in many cases,
practically important. Humans can provide example sentence completions to bias
an AI towards producing a preferred continuation, which can benefit search
rationalization, user service, and knowledge dissemination. Users might craft
prompt-driven examples that guide AI generation to exhibit strange or harmful
behavior in text adventure games or chatbots.

1.1. Defining Prompt Engineering

So, first and foremost, it is crucial to define prompt
engineering and all its implications. Prompt engineering is a method or an
approach, if you will. It is used to improve or indeed manipulate the output of
an artificial intelligence model or system. This can be done via the input of
certain prompts, which can often greatly influence the behavior of the model.
This method comes with a lack of requirement for directly altering the
implemented AI model, as well as the need for further changes to the system.
The nature and work of the AI in question can be left virtually untouched,
while the designed prompt is key to the complete application work. This can, in
some cases, even replace the need for further machine learning and additional
inputs of that nature.

There are certain cues that a prompt, as previously
mentioned, can give to an artificial intelligence system. These cues can
heavily influence the desired output, thus they are to be designed specifically
with the final result in mind. Furthermore, manipulating a prompt can pave the
way to the design of what is known as a Lean AI, a simpler version with
improved output. The process of prompt engineering can involve knowledge from
exceptional linguistic and psychological realms, all in order to then create an
improved implementation based on the established knowledge from the previous
prompts. Finally, it is worth mentioning that the application of the designed
prompts can lead to skills and much-needed further AI knowledge, offering an
indirect benefit as well. The prompts used can help to gain a better
understanding and insight into how the AI in question works, bringing new
knowledge in this scientific and interdisciplinary effort.

2. The Importance of Prompt Engineering

A critical idea that has been missing from public
discussions about AI is the importance of prompt engineering. Good prompts lead
to systems that have better performance, generate results that are more useful
for human users, and exhibit more human-like behavior. This is likely to be
especially important in the near future, as systems have so far been most
limited by how they are prompted and steered, rather than by their
capabilities.

Prompt engineering can directly address the difference
between human users putting the right things in and AI being able to do things
right. Thinking about inputs, as well as the process of coming up with good
inputs, is directly useful in reducing error and increasing efficiency. The
deployment of AI systems in a wide range of human-facing roles, like tutoring
and web search, benefits greatly from prompts that make the behavior of the
model match the human user's expectations. We have already seen this
experimentally with policies: while human-like behavior is hard to achieve
through imitation learning alone, manually constructed heuristics and rewards
focusing on social behavior have served to mitigate this limitation. We should
anticipate that the same will be true for language models. We should expect any
one-size-fits-all heavily biased generative models to be more coherent and
produce relevant responses if prompted to closely resemble their human trainer.
Ultimately, prompt engineering is a valuable skill for companies that
productize AI systems and researchers who aim to generate better-behaved
systems. While the craft of engineering a good prompt at present is getting
overlooked, competing now will likely demonstrate long-term benefits.

3. Diverse and Innovative Applications of Prompt Engineering
within the Field of AI

Up to now, we have been discussing the concept of prompt
engineering abstractly, with special attention to how it can be used to enhance
AI’s inner workings and outcomes. For this section, we take a practical
approach and examine the numerous contexts in which stratified and structured
prompts are useful. We conclude by showing how the benefits of stratified
prompts in one case study may vary depending on what the prompt-user community
wishes to do.

3.Applications of Prompt Engineering in AI

Any AI application that relies on edge cases can benefit
from using a prompt, and better outcomes are possible if the provided prompt is
stratified or specifically designed for the problem at hand. Naturally
occurring examples of stratified prompts can be found in various domains,
including but not limited to medicine, business, and pedagogy. In many of these
domains, the prompt designer’s goal is to support diverse groups of users in
some cognitive activity, from decision-making to learning. In each domain, we
summarize critical topics for which stratified prompts can be designed, the top
users each prompt must be stratified across, and important questions to address
in prompt design.

Healthcare: important topics include disease progression,
diagnosis, risk assessment, treatment effectiveness, and medication adherence;
further stratification by indicator can also be appropriate. Business:
important topics include economic forecasting, identifying potential employees
and clients, and monitoring; stratifying across protected classes is
imperative, but stratification across other characteristics can be equally
important. Reinforcement Learning: important topics include safely deploying
agents in real-world and sim-to-real settings; stratification is essential when
communicating results to the general public. Scaling Reinforcement Learning to
Real-World Problems: potential topics include computational efficiency,
scalability with respect to real-world complexity, incorporating prior
knowledge, and diversity of real-world applications; stratification can be
important when discussing applications or domain experts.

3.1. Natural Language Processing

In the domain of natural language processing (NLP), prompt
engineering has shown promise in shaping various aspects of language models.
Since machines understand language as input, prompt engineering is beneficial
in this domain. Translation, summarization, transformation, and question
transformation are some of the tasks wherein prompts can significantly impact
both language generation and comprehension. There are now a variety of
frameworks that include prompt-based techniques. The techniques involve a
feature of analogy with humans in synthesizing language, working best when
drawn from the same social or linguistic background. The authorship of prompts
needs to be harmonized with the dialect of the training data.

Asking machines to imitate humans is gradually functioning
as a guiding principle in NLP. There is a direct benefit of machine learning
from social cues in speech-making and interpretation, especially for tasks
related to human language, including translation and sentiment analysis.
Consequently, prompt engineering features in paradigms that focus on developing
language models such as few-shot learning for a variety of applications,
classification, and generation tasks. In a variety of such implementations,
prompt engineering has been instrumental in demonstrating that language
prediction pertains to augmenting language generations. However, prompt-based
techniques in language generation are not yet a standard procedure because
credible generation tasks challenge prompt-engineered systems.

In a few-shot classification task, effective prompts have
made operations simpler when used in combination with classifiers. Prompts can
also simplify text generation, which, when evaluated, achieved a high score
when compared with completion-based generation. Furthermore, when the few-shot
task itself is learning to generate a summary, it has been shown to also
generate results that transcend state-of-the-art results. This also makes
learning to control a model to generate polite requests a challenge, given that
most prompts have been sent anonymously. One of the technical challenges with
prompts in language generation is distinguishing turns of human and AI speakers
in dialogue. The language model and token length have been found to be
constraints when executing this task. Techniques lack linguistic knowledge and
cannot generate effective prompts when negotiating dialogue. The situation in
dialogue is the same as for prompts in NLP; when the UI and LM are tasked to
play negotiation games, providing a dialogue utterance between the two, the
most difficult part is generating prompts at the dialogue training stage. There
is a correlation in the data that shows how the length of an effective prompt
is based on linguistic knowledge of the dialogue, with this feature being
shared with dialogue efficiency.

3.2. AI Chatbots

Providing an interface for natural language conversational
interactions, AI chatbots offer a broad area of prompt engineering
applications. An especially important aspect of any chatbot design is the
method used to generate prompts. The prompts used by chatbots significantly
contribute to their interaction quality. This is particularly true for language
models oriented towards human-like conversational skills, where the objective
is to first and foremost ensure their engagement as a conversation partner.
Feelings of the easy "flow" of conversation reduce frustration and
increase user satisfaction. Furthermore, in attempting to imitate human
conversation as closely as possible, multiple conversational strategies often
exist for reaching common conversational outcomes. In this way, the prompt can
shape the conversational strategies used. Subsequently, prompt choices can
influence the overall chatbot conversational style.

AI chatbots have been developed in capacities ranging from
personal assistants to supporting human resources in educational platforms or
customer service. They aim to provide a human-like conversational experience by
employing a task-based conversational strategy. A conversational turn consists
of a user input and the system's prompt. The core concern of designing prompts
is to make them generally contextually relevant, semantically meaningful, and
lexically varied. The relationship between the user input, for which the
chatbot input is a response, and the response is crucial. When generating
responses, chatbots have to adapt to the speakers' input in an appropriate
context within the chat. However, across multi-turn conversations, maintaining
these factors can be complex, especially when handling difficult user inputs.
The context and history of a conversation can have a profound effect on
deciphering the response's generation. It is because of this combination of
considerations that engineering prompts in chatbots is regarded as a
non-trivial and complete conversational design problem. In recent years,
several chatbots have been wisely engineered using prompt generation after a
large amount of iterative user judgment assessments.

4. Challenges and Future Directions

Prompted by these possibilities and lacking effective and
comprehensive search algorithms, it becomes necessary to improve our ability to
handcraft effectively performing prompts. It needs to be noted that AIs are not
yet primarily programmed with the goal of understanding human prompts and
answering them effectively. Consequently, an important part of prompt
engineering appears to be about learning to design effective prompts, a
challenge that requires an in-depth understanding of both natural language
processing and natural language understanding. We need to design prompts for
systems that do not currently possess a full understanding of the subject
matter, make prediction systems that understand, and potentially have an
ability to reason about language at an appropriate level of understanding for a
given task, and could eventually approach full artificial intuition. Challenges.
There are several challenges that should be addressed for efficient prompt
engineering. Just like other AI systems, AI models are not perfect, and they
can be biased, limited in their language understanding, and have other
artifacts. Although humans can help to pick the right answers within a range of
sensible interpretations or write hard-to-interpret automatically processed
text, they might also intentionally or unintentionally misuse prompts by
designing them to suggest incorrect beliefs. The response of generative
language models depends on their internal randomness; thus, the models can give
different, sometimes inconsistent answers, making selection tasks hard. All
these challenges make prompt design a difficult and largely data-demanding problem.
Future Directions. One possible future goal is to create human or at least
plausible prompts at the model level with the capability of being effective
across a range of humans. An open question concerns whether quantifiable
structuring semantics for well-posed questions are sometimes difficult to pose.
Thus, the model design goal requires models to discover the prompts or
semantically similar variants and effectively answer those permutations.
Additionally, successful prompt engineering would conceivably guide prompts to
avoid indirectly misleading the human proctor either as stated or through
implications or effects of implications. This gives rise to opportunities for
interdisciplinary interactive design among human-AI interface techniques,
security, and linguistics. The conclusion is that prompt design is a
foundational problem that grows in importance with advancing AI, human-AI
collaborations, and living and working more in virtual environments with
NLP-based interfaces, making increasing demands on collective expertise.

5. Conclusion

In conclusion, we explored the emerging concept of prompt
engineering and what it means in the context of current AI research and
development. We defined important terms and discussed the potential utilities
of effective prompts. Taking a user-focused approach, we emphasized that the
prompt can influence users' perceptions of and interactions with the model
generating the response. Furthermore, a suitable prompt can guide users towards
an informative, productive submission. Despite clear potential benefits, some
pitfalls do render designing effective prompts a challenge. A number of
empirical and theoretical results were presented to illustrate how prompts can
steer the outcomes of generative and retrieval-based systems. We also opined
that continued exploration and development in this space is an important
pursuit for the future of AI, even as the systems, data, models, and tasks
evolve over time.

We must continue to design and conduct experiments with
interactive setups and be quick to adapt to the rapidly changing settings and
expectations of both modern models and their users. Best practices and robust
mechanisms for evaluating prompt performance should also continue to be
developed. It is crucial that when designing prompts, or attempting to flatten
them, we always consider the resulting implications on the users interacting
with them. Ethical considerations are fundamentally important for this and all
other discussions that impact the use, development, deployment, and access to
AI technologies. Lastly, more interdisciplinary efforts should be encouraged in
working towards multimodal developments, like fact ordering, and in developing
more interesting and practical applications that can benefit from these
prompt-design techniques. The concept of prompt engineering should be discussed
and adopted more widely, and future work should continue to explore and
describe this part of the AI pipelines in more depth. This area is fast-moving,
and the dialogue and investigations need to keep pace.