What is prompt engineering?

Generative AI models are built on transformer architectures, which enable them to grasp the intricacies of language and process vast amounts of data through neural networks. AI prompt engineering helps mold the model’s output, helping ensure the artificial intelligence responds meaningfully and coherently. Several prompting techniques help ensure that AI models generate helpful responses, including tokenization, model parameter tuning and top-k sampling.

Prompt engineering is proving vital for unleashing the full potential of the foundation models that power generative AI. Foundation models are large language models (LLMs) built on transformer architecture and packed with all the information the generative AI system needs.

Generative AI models operate based on natural language processing (NLP) and use natural language inputs to produce complex results. The underlying data science preparations, transformer architectures and machine learning algorithms enable these models to understand language and then use massive datasets to create text or image outputs.

Text-to-image generative AI like DALL-E and Midjourney uses an LLM in concert with stable diffusion, a model that excels at generating images from text descriptions. Effective prompt engineering combines technical knowledge with a deep understanding of natural language, vocabulary and context to produce optimal outputs with few revisions.

Prompt engineering techniques involve strategies to guide generative AI models in producing desired outputs. These techniques include zero-shot prompting, where the model is given a task it hasn’t been explicitly trained on, and few-shot prompting, which provides the model with sample outputs to clarify expectations. Another key technique is chain-of-thought prompting, which breaks down complex tasks into step-by-step reasoning to improve the AI's understanding and accuracy. These approaches help ensure that the AI model generates more coherent and relevant responses.

The primary benefit of prompt engineering is the ability to achieve optimized outputs with minimal postgeneration effort. Generative AI outputs can be mixed in quality, often requiring skilled practitioners to review and revise. By crafting precise prompts, prompt engineers help ensure that AI-generated output aligns with the desired goals and criteria, reducing the need for extensive postprocessing.

It is also the purview of the prompt engineer to understand how to get the best results out of the variety of gen AI models on the market. For example, writing prompts for Open AI’s GPT-3 or GPT-4 differs from writing prompts for Google Bard. Bard can access information through Google Search, so it can be instructed to integrate more up-to-date information into its results. However, ChatGPT is the better tool for ingesting and summarizing text, as that was its primary design function. Well-crafted prompts guide AI models to create more relevant, accurate and personalized responses. Because AI systems evolve with use, highly engineered prompts make long-term interactions with AI more efficient and satisfying.

Clever prompt engineers working in open-source environments are pushing generative AI to do incredible things not necessarily a part of their initial design scope and are producing some surprising real-world results. For example, researchers developed a new AI system that can translate language without being trained on a parallel text. Engineers are embedding generative AI in games to engage human players in truly responsive storytelling and even to gain accurate new insights into the astronomical phenomena of black holes. Prompt engineering will become even more critical as generative AI systems grow in scope and complexity.

Large technology organizations are hiring prompt engineers to develop new creative content, answer complex questions and improve machine translation and NLP tasks. Skills prompt engineers should have include:

• Familiarity with large language models: Understanding how large language models (LLMs) work, including their capabilities and limitations, is essential for crafting effective prompts and optimizing AI outputs.

• Strong communication skills: Clear and effective communication is vital for defining goals, providing precise instructions to AI models and collaborating with multidisciplinary teams.

• The ability to explain technical concepts: Prompt engineers must be able to translate complex technical concepts into understandable prompts and articulate AI system behavior to nontechnical stakeholders.

• Programming expertise (particularly in Python): Proficiency in programming languages like Python is valuable for interacting with APIs, customizing AI solutions and automating workflows.

• A firm grasp of data structures and algorithms: Knowledge of data structures and algorithms helps in optimizing prompts and understanding the underlying mechanisms of generative AI systems.

• Creativity and a realistic assessment of the benefits and risks of new technologies: Creativity is important for designing innovative and effective prompts, while a realistic understanding of risks helps ensure the responsible and ethical use of AI technologies.

In addition to these skills, prompt engineers can employ advanced techniques to improve the model’s understanding and output quality:

• Zero-shot prompting: This technique provides the machine learning model with a task that it hasn’t explicitly been trained on. It tests the model’s ability to produce relevant outputs without relying on prior examples.

• Few-shot prompting: In this approach, the model is given a few sample outputs (shots) to help it learn what the requestor wants it to do. Having context to draw on helps the model better understand the desired output.

• Chain-of-thought prompting (CoT): This advanced technique provides step-by-step reasoning for the model to follow. Breaking down a complex task into intermediate steps, or “chains of reasoning,” helps the model achieve better language understanding and create more accurate outputs.

While models are trained in multiple languages, English is often the primary language used to train generative AI. Prompt engineers will need a deep understanding of vocabulary, nuance, phrasing, context and linguistics because every word in a prompt can influence the outcome.

Prompt engineers should also know how to effectively convey the necessary context, instructions, content or data to the AI model.

If the goal is to generate code, a prompt engineer must understand coding principles and programming languages. Those working with image generators should know art history, photography and film terms. Those generating language context might need to know various narrative styles or literary theories.

In addition to a breadth of communication skills, prompt engineers need to understand generative AI tools and the deep learning frameworks that guide their decision-making.

A prompt engineer designs, tests and refines prompts to optimize the performance of generative AI models. They work closely with AI systems to create queries that elicit accurate, relevant and creative responses. Their responsibilities include understanding the capabilities and limitations of different AI models, experimenting with advanced techniques such as zero-shot and few-shot prompting, and collaborating with teams to apply AI in real-world scenarios. Essentially, a prompt engineer bridges the gap between AI technology and practical applications.

To get the best results from generative AI, prompt engineers should focus on crafting clear, concise and context-rich prompts. Using specific instructions and examples can help guide the AI to generate the desired output. Iteratively refining prompts based on the model’s responses allows engineers to improve results further. Additionally, understanding the limitations of the AI model and tailoring prompts accordingly can prevent errors or biased outputs. Finally, testing prompts across various scenarios helps ensure robustness and reliability.

As generative AI becomes more accessible, organizations are discovering new and innovative ways to use prompt engineering to solve real-world problems.