What is Prompt Engineering? AI Prompt Engineering Tools & Examples

The definition of Prompt Engineering

Prompt engineering is a process of generating specific outputs and results using a combination of generative artificial intelligence and human guidance. Generative AI can only try to mimic human output when controlled and guided using detailed instructions. Understanding what prompts, words, and phrases are the most relevant for the sake of creating a specific output is the brunt of prompt engineering. A combination of “trial and error” and creativity is how prompt engineers find the most compelling phrases and commands for generative AI systems.

Prompt engineering itself is made possible by a process called “in-context learning” – the ability of a Large Language Model (LLM) to learn temporary information from previous prompts. The existence of an added context provided through “learning” allows for the AI responses to be more accurate, informed, and context-sensitive. In-context learning also carries the advantage of all this additional knowledge being completely temporary – it does not translate between one chain of prompts and another, creating plenty of opportunities to receive an unbiased response from different standpoints.

The definitions of a Prompt and a Generative AI

Two key elements are instrumental for the success of prompt engineering as a process – a generative AI and a prompt. A generative AI is a solution based on artificial intelligence capable of generating new content – images, videos, music, stories, conversations, or simple text answers. A prompt is a text fragment in a natural language that the generative AI uses as a command to perform a specific operation.

Generative AIs are powered using a new version of ML algorithms with extremely large data sets, so their name is directly transcribed to Large Language Models. These models are extremely powerful and flexible, capable of answering questions, translating texts, summarizing documents, etc. 

Naturally, one might think that interacting with these models is a highly sophisticated process. And yet, the inherent nature of LLMs makes them extremely easy to work with since the system can respond to practically any input, including single words. With that being said, receiving valuable answers and responses is not always easy, and a prompt engineer would have to keep refining existing prompts and testing new ones to keep up the quality of the AI responses as time goes on.

Prompt Engineering use cases

The most prominent use case of prompt engineering is in extremely complicated AI systems, and the main goal is to improve the overall end-user experience when interacting with an LLM. Most of the use case groups in prompt engineering can be separated into multiple groups: 

• Analysis and reasoning

Complex task solving was always one of the biggest use cases for AI and LLM. The ability to analyze and evaluate an abundance of information on short notice is paramount for such models – and prompt engineering can enhance the capabilities of an existing system in this regard. 

• Creative scenarios

Problem-solving can have multiple iterations to itself. It may be a sophisticated task as it is, or it can be something as seemingly straightforward as idea generation. Modern LLM systems are fully capable of generating concepts, solutions, and ideas on a specific topic – and the results of this kind of generation can also be significantly enhanced with the help of a prompt engineer. 

• References and expertise

Subject matter expertise may be one of AI's most common use cases right now. Ensuring accurate and up-to-date responses is paramount for any AI that can offer such responses. Luckily, prompt engineers with experience in specific fields make it possible for AI answers to be fine-tuned for higher accuracy, better answer framing, and so on.

• Application development

Artificial Intelligence can offer a massive advantage to many existing applications, be it in terms of user experience, customer support, etc. Not only can AI elements be integrated into existing applications to a certain degree, but there is also the possibility of creating entirely new apps from scratch using nothing but an AI model and an expertly crafted template. The prompt engineer can operate with the template in question in order to ensure the accuracy and usefulness of AI responses on the topic, among other situations.

Prompt Engineering examples

Since prompt engineering is a complementary process to AI and LLM solutions, there is a lot of overlap between common examples and use cases. However, there are still too many examples to list, so we choose to present them in a specific fashion based on groups.

1. Personalization and recommendations are quickly gaining popularity when it comes to customer interactions. Plenty of research has showcased how a personalized experience dramatically increases the chance of a customer becoming a repeat buyer. Extensive personalization, on the other hand, is what AI can offer to all clients en masse. An AI model can retrieve the product preferences of a specific customer and create a list of recommendations based on that information.
2. Language generation in a specific field expands upon one of LLMs' most traditional use cases. There are plenty of highly specific fields of work all over the planet that require delicate and thorough information available at all times. A regular LLM model would struggle with such information – but a model that was trained to work in a specific field from the get-go would be that much more effective. A notable example of that is a Med-PaLM large language model from Google – a highly specialized model that was trained and learned from various medical questions, now acting as an extremely useful source of medical information from different areas of expertise.
3. Natural Language Processing tasks are some of the most apparent examples of prompt engineering use cases. NLP itself is used for AI models to understand the meaning of the user prompt. The examples for this group are “summarize this article” or “provide a short overview of this text”. NLP is at its best when analyzing existing information in text form and presenting it in a different fashion.
4. Virtual assistance and chatbots are also relatively common now regarding AI use cases that prompt engineering can enhance. If a chatbot is working in a specific field, then it should not be difficult to create a list of the most common questions for this field. That way, a prompt engineer can refine and test various prompts for these questions to provide a better user experience.
5. Data analysis is a somewhat expected use case for AI (and prompt engineering) – the ability to analyze and draw insights from an existing text for better analytical results across the board. The aforementioned Med-PaLM model from Google is an excellent example of how a specifically trained model can be an incredible source of highly specialized information on the topic.

The Importance of Prompt Engineering

Introducing generative AI to the public led to a drastic increase in the prompt engineering job sphere. The main goal of most prompt engineers is to lessen the “distance” between the LLM and the end user in terms of understanding. Many different templates and scripts are used to provide context to the LLM when it comes to specific requests, offering better generative results as an outcome.

Prompt engineering is made to simplify and increase the existing AI applications and use cases. One of the most common ways of processing average user input is to encapsulate it in a prompt before passing it to the LLM. The existence of a prompt provides regular user input with a lot of context to personalize and expand the result.

The advantages of Prompt Engineering

Prompt engineering may offer a multitude of advantages to the AI industry. These advantages are vast and complex, including: 

• Flexibility boost

Both the scalability and flexibility of AI organizations are parameters that can be greatly improved using prompt engineering. Not all templates and prompts must be created for a specific context – they can also be broad, highlighting patterns and logical links. That way, a single prompt can be reused multiple times within the same company, significantly improving LLM’s capabilities in this regard.

• Higher level of developer control

The existence of prompt engineering as an industry dramatically expands the ability of developers to control user interactions with LLMs. Good prompts can offer context and intent for the AI, refining the end result of an output and its presentation. Alternatively, it can also be used as a control tool over unauthorized or even illegal content – limiting end users when it comes to generating inappropriate content, for example.

• Better user experience

Mitigating potential human bias or inaccurate answers is another large element in prompt engineering advantages. Trial and error is a relatively standard way of learning how to interact with an LLM. Suppose the prompt engineer already went through this step, creating specific prompt templates in the process. In that case, the end user does not have to go through it at all, significantly improving the overall user experience. The ability to create templates and presets for specific groups of requests also makes it easier for AI to provide more accurate and helpful results to end users.

Best practices for Prompt Engineering

Prompt engineering can be a surprisingly difficult task. It requires a delicate set of instructions to be crafted, combining scope, context, and expectations in one package. And yet, generating better results is still possible for most users if they follow the best practices described below.

Keeping a delicate balance between complexity and oversimplification is one such tactic. A simple prompt may result in an answer that is too general and abstract to be helpful. A complicated prompt, on the other hand, may result in a completely wrong answer, as well. If the prompt's topic is not particularly common or especially complex, then the chances of an incorrect answer are even higher. As such, using straightforward language in moderation without bloating the answer in size is the way to go.

Avoiding ambiguity is another significant advice when it comes to prompt engineering. It may be somewhat tricky in the context of previous advice, but remember that AI may not have enough context by itself to provide the correct answer based on the prompt. Alternatively, a specific and direct prompt would have a much better chance of receiving a correct and valuable response.

Refining the prompt with trial and error is, in some way, an inevitable part of prompt engineering. The entire process of prompt engineering is iterative by nature; it relies on experimenting and trying out different ideas to find the best possible option or combination of options. As such, the ongoing process of testing and refining is how the prompts are getting better, more accurate, more helpful, and less ambiguous to end users.

Providing context to the prompt is another immense contributor to the overall response quality. The existence of appropriate context works as a stabilizer and a concentrator of the AI/LLM response, presenting a much more specific and less vague response to the prompt.

Prompt Engineering techniques

The field of prompt engineering is vast and reactive, evolving and improving itself regularly. This fact makes it difficult to showcase every technique used in the field. As such, we are going to present some of the more commonly used examples instead.

Generated knowledge prompting solves a single problem by generating relevant facts about the problem in question. These facts are then used to complete the original prompt, offering better data quality as a result. Self-refine prompting is using a continuous loop of generating an answer and creating a critique for it. That critique is then used to create a new answer, which will also be critiqued, and so on. This kind of prompting only stops when it reaches a specific reason to stop that was determined earlier – the usage of a stop token or the user running out of tokens.

Chain-of-thought prompting separates the original question into several smaller ones and answers all of these questions instead, expanding and improving the response. These smaller questions are also compared with each other and used to form a definite conclusion based on multiple responses to the same question. Complexity-based prompting expands upon the idea of chain-of-thought prompting by simultaneously performing several of these processes. These prompts are then evaluated for the total length of the “chain of thought”, as well as for what conclusion was the most commonly reached one. This conclusion is what the answer to the prompt is at the end.

Of course, this list is far from conclusive, and plenty of other techniques are used in prompt engineering regularly – least-to-most prompting, maieutic prompting, tree-of-thought prompting, directional-stimulus prompting, etc. However, the four techniques explained above should be enough to show how prompt engineering relies on multiple tactics and approaches to solve its issues and questions.

Prompt Engineering tools

Prompt engineering is not particularly complicated in its base form, but it tends to get relatively complicated as time goes on. As AI models and their input grows, it becomes more and more difficult to manage, control, and modify existing prompts by hand. Luckily, there is an entire market full of exemplary AI prompt engineering solutions that can help users with templates, prompts, built-in customization tools, and more. These tools are paramount when it comes to formulating detailed prompts and experimenting with them.

Lazy AI

Lazy AI is an outstanding application development platform that offers no-code development capabilities with the help of dedicated templates. It is an impressive prompt engineering solution that relies on an extremely powerful AI core that can create custom applications with absolutely no coding knowledge involved.

Lazy AI uses a multitude of pre-built customizable templates in order to create various application types - chatbots, web apps, mobile apps, etc. The ease and speed of app creation make it a lot easier to prototype and test ideas. Lazy AI can also be integrated with many other services and APIs to expand its functionality and simplify complicated workflows.

Azure PromptFlow

Azure PromptFlow is a highly sophisticated LLM development process software capable of supporting various prompt engineering projects in this sphere. PromptFlow is a multifunctional environment combining built-in tools, templates, natural language prompts, and Python code. 

Not only can it be used for workflow management with its Directed Acrylic Graph view, but it can also provide a fast and responsive environment for testing multiple prompt environments simultaneously. These deployments are monitored on a constant basis, giving plenty of information to developers when they need it.

Jupyter Notebooks

Jupyter Notebooks is a comprehensive Python interpreter that allows for real-time code execution while also allowing for connecting with multiple data sources and providing seamless integration with existing codebases. It is an excellent platform for working with data-intensive workflows using iterative coding and exploratory analysis.

It is worth noting that Jupyter requires external libraries to expand its capabilities in some areas (model versioning or experiment tracking, for example). Jupyter works excellently with other AI software, such as LLMStudio, complementing the software’s capabilities with managing ML lifecycle, tracking model evolution, keeping logs of experiments, etc.

LLMStudio 

LLMStudio is a development platform from a relatively popular company, TensorOps, that offers plenty of tools for AI app developers who work with LLMs. LLMStudio tries to make it easier to integrate LLM elements in various applications, and it can offer four main features – Python SDK + REST API, LLM Gateway, Prompt storage, and Graphical Studio.

Support of REST API and Python SDK makes integrating the solution in any backend code easier, and LLM Gateway offers centralized access to multiple LLMs in the exact location. Prompt storage exists to store past LLM requests in the form of a Postgres database, and Graphical Studio acts as a convenient web UI for all of these capabilities.

LangSmith

LangSmith attempts to simplify the process of evaluating, debugging, testing, and monitoring LLM applications, making it easier to transform a prototype into an application that is ready for production. It supports data export in multiple formats (OpenAI-compatible), offering relatively simple model fine-tuning. 

LangSmith offers quick error notification and resolution, makes it easy to work with test data sets, and can be easily integrated with evaluation modules. Multiple LLM app development stages can be operated and managed using LangSmith as a centralized hub. It is capable of monitoring a multitude of application parameters – system performance or some other metrics such as user interactions.

Prompt Engineering and the Future

Artificial Intelligence is an industry that has been developing extremely fast as of recently. It is a vast and complicated field that is still in its active expansion stage, providing various developments and new practices regularly. Prompt engineering is a very important bridge between humans and machines, providing better results to end users than ever before. It is easy to see how the value of prompt engineering will grow even more soon as various forms of AI are getting more and more integrated into regular people’s lives.

There is also the aspect of software development that is closely tied to the topic of AI and prompt engineering. The ongoing exponential growth of the developer numbers makes it easy to see how solutions like Lazy AI will become more and more relevant in the near future. The ability to offer no-code application development capabilities drops the skill entry bar for app development lower than ever before, providing millions of people with the ability to develop applications to their own needs without spending months on learning coding skills.

Using configured AI models and templates to create entire applications is as close to the definition of prompt engineering as it gets – using natural language prompts to modify or specify various details of an app development process. 

As time goes on, AI models will become more and more sophisticated to the end user. Prompt engineers exist to simplify the process of communicating with AI models on any kind of topic. Investing in prompt engineering as a field of knowledge is not just about improving AI-related results; it is also about being prepared for the foreseeable future when prompt engineering becomes even more critical than it is now, changing lives and improving user experiences in many different forms.

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