In this article, we'll be looking at the introduction to prompt engineering, examples, its techniques, how to get started and learning resources etc.
In recent times, there have been innovative discoveries and applications of Artificial intelligence in solving problems and aiding faster access to quality outputs within a short time to meet a desired action. However, humans need tools to enable them to interact with the language models of AI(Artificial Intelligence). These AI tools provide accurate answers based on the accuracy of the prompts. ChatGPT is an example of an AI tool that works based on user prompts.
What is prompt?
Prompt in simple terms refers to an action to trigger a response in the form of a question, a phrase or query. In the context of AI, it is used to convey context or instructions to a language model to accomplish task(s).
Example of a prompt:
Prompts are not limited to language models but can also be applied to image generation, stable defusion and code generation or completion.
A Prompt is made up of any of the following elements depending on the goal(s) to achieve:
Instructions: A particular action you want the model to take.
Context: can incorporate outside data or additional context, which helps direct the model to provide superior results.
Input data: is a request or query for which we desire to find a solution.
Output indicator: identifies the format or type of result.
What is Engineering?
Engineering is the area of science and technology that deals with creating, using, and maintaining machines / systems, buildings, and engines. It is the science of generating new technology and infrastructure as well as resolving technological issues.
For example, Software Engineering focuses on creating, modifying, and maintaining software programs.
What is Prompt Engineering?
Prompt Engineering is the art of creating and refining prompts to efficiently use language models(LMs) for a range of applications. It involves constructive design of input that helps the model to match the context and give a quality and accurate result.
For example, I'll relate this to the job of a librarian. There are different techniques for storing information resources in the library (cataloging and classification) and principles for access (cards or retrieval codes). See the Librarian as a prompt engineer (who has studied the Library models for information storage, and retrieval and knows how best to retrieve accurate information for specific prompts) who serves as an interface between the library collection (language models) and the library user query (prompts) to be satisfied with a quality result.
Another way of prompt engineering in the context of a library is when the librarian or desk officer defines questions (prompts) to help collect library user experiences about the library services which facilitates excellent improvement of the models to provide more quality services in the future. This implies that more prompts can improve and fine tune the models.
All of these processes can be referred to as prompt engineering. The more the library requests, the more the librarian organizes skills to be able to improve the principles of storage and retrieval that can provide and aid faster access to more precise and accurate results (desired output).
Prompt Engineering is the process of defining the prompts to language models (AI systems) using precise methods such as ChatGPT to get quality results. The accuracy of results is dependent on the accuracy of the prompts.
The interaction and use of language models (LMs) for different applications gave rise to the prompt engineering discipline which involves developing, refining and optimizing prompts in a structured way for quality results. It makes communication between humans and Large language models (AI) easy. Just like in the case of a Library, a librarian and library user.
An example of prompt engineering in action is the interaction with GPT (Generative Pre-trained Transformer) a large language model example by writing accurate and intelligent text-based scripts to get expected results.
LLM (Large Language models) is a deep learning algorithm that can be used to carry out various natural processing language (NLP) tasks. LLM can likened to an advanced setting of a Library system both in principles and collection of data (trained data).
Types of Prompt Engineering
Different types of prompt engineering can be used when interacting with large language models.
Text completion prompts: prompts that trigger auto-completion of sentence or phrase.
Instruction-based prompts: prompts with clear instructions to give a guide to AI for quality responses.
Multiple-choice prompts: This prompt helps limit the language model's output. The output can be limited to select the most suitable response by providing a variety of choices and asking the model to limit itself to a single answer.
Contextual prompts: give the language model contextual hints.
Bias mitigation prompts: To remove prejudice, these prompts assist a large language model in refining its result.
Fine-tuning and interactive prompts: By analyzing the output and altering the query to enhance the output and model performance, this kind of prompting iterative improvements of prompts.
Prompt Engineering Techniques | Strategies
Chain-of-Thought (CoT) Prompting
Self-criticism | Self ask
Program Aided Language Model (PAL)
Generated Knowledge Prompting
Automatic Reasoning & Tool Use (ART)
Advantages of Prompt Engineering
Innovative and groundbreaking technologies in Natural language processing (NLP).
Prompt engineering skills help to improve the effective and efficient use of language models by AI engineers and researchers.
Important for innovative research and the explorative advancement of applications built using language models.
Contributions to multiple industries and community needs.
Indispensable value offer (research professionals, high salaries, job opportunities)
There are few communities to figure out solutions to issues (coding, defining or redefining errors, access to vetted prompts skills)
Complexity of language models.
Limited model knowledge.
Non-availability of useful data to help provide quality answers to prompts of users.
The challenge of designing clear and effective prompts.
The iterative process of defining accurate prompts is tasking.
Who is a Prompt Engineer?
A prompt engineer develops prompts that compel a neural network to respond logically. They help to ensure that the user's query is best understood by the robot to give an accurate answer. A prompt engineer has a good grasp of how to query LLM (large language model) to provide the desired output.
Prompt Engineers can also be referred to as AI operators because they provide AI (simulation of human intelligence processes by machines) with a fine-tuned query to get accurate expected results.
The reliability and precision of prompt collections are maintained by a prompt engineer.
Prompt engineering skills
Ability to research.
Writing and refining content.
Cognitive and Creativity.
Familiarity with language models and linguistics.
Ability to generate good questions for quality answers.
Knowledge of software development practices.
Attention to detail.
Understanding of prompting techniques.
Coding (not compulsory in specific aspects of prompt engineering but recommended)
Consistency and staying updated with trends in AI development.
Resources to become a prompt engineer
Vanderbilt Data Science : Dr Jules White (Vanderbilt University - professional in the world of AI)
In this article, we've taken an in-depth study of prompt engineering, a new discipline that facilitates effective communication with Artificial Intelligence tools. It's all about crafting precise prompts from the understanding of how models respond to them through various techniques and skills. The repetition of redefining prompts makes these models better and enables them to give more accurate outputs to meet desired outcomes.
There are lots of opportunities for prompt engineers due to the innovative and numerous applications of prompt engineering in different spheres of human endeavor. With the ground breaking solutions from large language models, skills in understanding how to craft prompts will be in high demand.
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