AI prompt engineering is the process of giving a Large Language Model (LLM) effective instructions for generating accurate responses to user queries.
If AI prompts guide an LLM in answering a particular question, AI prompt engineering is the art of creating those prompts. With the right AI prompt engineering, your LLM can generate text, translate languages, summarize information, or answer questions much more effectively.
AI prompt engineering touches all prompt components:
Instruction
The prompt’s instruction clearly defines the desired action from the LLM (e.g., "Write a blog about AI prompt engineering").
Context
Additional context can be added to the prompt to help the model better understand the task and generate a more relevant and higher-quality response (e.g., "Write a blog about AI prompt engineering with emphasis on the technical challenges for the automotive industry.").
Output format
The prompt’s output format tailors the response to your needs (e.g., "Write a 1000-word blog about AI prompt engineering with emphasis on the technical challenges for the automotive industry, using bulleted text.").
Here are some examples of prompts for different types of common tasks:
Text generation: Write a story about a dog who can talk.
Translation: Translate 'Hello, how are you?' into Spanish.
Summarization: Summarize the article 'The History of Artificial Intelligence' in 100 words.
Question answering: What's the capital of Australia?
Every novice programmer is familiar with the term garbage in, garbage out. In other words, if your code is good, you get good results. If your code is bad, you don’t. AI prompt engineering is no different. It’s the process of creating effective instructions (prompts) for an LLM, to enable the model to generate the desired output.
AI prompt engineering is a specialized skill that requires an in-depth understanding of the capabilities and limitations of your LLM – and or your AI data readiness – to tailor prompts that will result in the most relevant and accurate responses. It’s a crucial function in the practical application of AI data to a wide range of tasks, since it directly influences the quality and usefulness of the generated content.
If your prompt engineers provide clear and concise instructions, they can make your LLM perform quickly and accurately. Poor prompting, on the other hand, results in poor responses. Since the effectiveness of your LLM essentially depends on the quality of your prompts, prompt engineering is a vital skill for your AI developers and users to develop.
The history of prompt engineering can be traced back to the early days of AI research, when data scientists were testing various ways of interacting with an enterprise LLM. The importance of prompting grew as the models became more sophisticated. Today, AI prompt engineering is a dynamic and evolving field in which new techniques, such as the use of LLM agents and functions, are continuously being tested and validated.
The key best practices for AI prompt engineering include:
Clarity
A well-crafted prompt is clear and unambiguous, and carefully avoids vague or generic terms. It needs to specify the desired task, the output format, and any relevant constraints. For example, instead of "Write a story," a clearer and more specific prompt would be "Write a 300-word children’s story about a robot who dreams of becoming a chef."
Relevance
Relevance helps your LLM better understand the task, and thus generate more accurate responses. It can include background information, specific examples, or references to related topics. For example, when requesting a translation, the prompt should define the original language, the target language (including dialect or region), and the style of the response (academic versus casual, for example) – to improve the effectiveness of the output.
Conciseness
Despite the need for relevance, a concise prompt is easier for an LLM to process and understand. So, your prompts should avoid unnecessary details or redundant information – instead focusing on the essential elements of the task and the desired outcome. For example, instead of saying, “Write a long, detailed essay about the history of artificial intelligence”, a more concise prompt would be “Summarize the history of artificial intelligence in 200 words”.
Creativity
LLMs are designed to generate creative outputs. Prompting should encourage this by experimenting with different prompt formats, styles, and techniques. Trying new approaches to see what results you can achieve is not only a recipe for fresh, unexpected responses, it can also be fun.
As AI moves mainstream and comes under increasing regulatory scrutiny, AI prompt engineering needs to be conducted with ethical considerations in mind. For example, prompts that promote harmful stereotypes, discrimination, or misinformation should be forbidden, to ensure that the generated content is fair, unbiased, and respectful.
Understanding and employing different type of prompt engineering techniques help you guide your LLM to produce more accurate, creative, and informative results. Some of the most common techniques include:
Few-shot learning
Few-shot learning provides your LLM with a small number of examples to guide its response. Analyzing these examples helps the model learn patterns and relationships that can later be applied to new data. For instance, when requesting an LLM to translate an English sentence into Spanish, providing a few examples of similar translations can help the model understand the context and produce the desired response.
Chain-of-thought prompting
Chain-of-thought prompting is an approach that guides your LLM through a series of intermediate steps to reach a solution. It’s generally used for complex tasks that require multiple steps. By breaking down the task into smaller, more manageable subsets, your LLM is better able to understand the problem and generate an appropriate output.
Role-playing
Role playing assigns your LLM a specific role or persona, which can help it generate more contextually relevant and engaging responses. For example, when asking your LLM to write a creative story, assigning it a specific role from the story could help it generate more believable dialogue.
Reinforcement Learning from Human Feedback (RLHF)
RLHF is a way of training your LLM to improve its performance based on human feedback. By rewarding the model for correct responses (good dog) and penalizing it for incorrect ones (bad dog), RLHF trains your LLM to generate better outputs over time.
Prompt libraries and templates
Prompt libraries and templates are pre-built collections of prompts that can be used on-demand for common tasks. While libraries can save time and effort (since the ready-made prompts have already been tested and refined), most prompts still need to be customized to suit your specific needs.
GenAI Data Fusion, the K2View solution for Retrieval-Augmented Generation (RAG) leverages AI prompt engineering to create contextual LLM prompts grounded in your enterprise data. For example, it uses chain-of-thought prompting to ensure that your RAG chatbot can access customer data to enable personalized interactions in real time – for more positive outcomes.
GenAI Data Fusion:
Accesses customer data to create more accurate and relevant prompts.
Masks PII (Personally Identifiable Information) dynamically.
Handles data service access requests and recommends real-time insights.
Connects to enterprise systems – via API, CDC, messaging, or streaming – to collect data from multiple source systems.
Thanks to RAG prompt engineering, GenAI Data Fusion powers your LLM to respond with AI personalization – leading to greater accuracy and relevance than ever before.
Discover AI Data Fusion, the RAG tools with built-in AI prompt engineering.