What is Prompt Engineering?

Prompt engineering is the process of crafting and optimizing prompts for generative AI models to get the right outcome. It includes understanding how large language models (LLMs) execute requests, how to make the model get your intent, and which context is needed to get the best response.

Why prompt engineering is important

Prompt engineering improves the accuracy and relevance of AI responses.
It reduces bias and the risks of getting irrelevant or inappropriate content.
This technique increases the predictability of AI behavior.

What is a prompt?

A prompt is an instruction for a task that an LLM should execute. It could be in different forms, from simple questions to detailed guidelines with samples. The primary purpose of the prompt is to get the answer to a specific problem and to let LLMs understand which response is expected.

What are the key elements of a prompt?

Prompt structure and style are crucial, since different models respond differently to specific formats.
Context and examples give an LLM far more information on which response is more relevant.
Multi-step interactions allow LLMs to get more context regarding your intent and enhance the results accordingly.

What are the types of prompts?

Zero-shot prompting is based on asking a direct question without any context, which is usually used for brainstorming and handling simple tasks like summarization.
Multi-shot prompts involve providing a model with a few pairs of inputs and outputs to give it the context before creating a final prompt.
Chain of Thought (CoT) prompting guides models to break down a complex task into multiple steps and, sometimes, includes reasoning for each of them to make the process more transparent.

The best AI prompt engineering practices

Prompt engineering best practices come in handy to create effective prompts, especially for complex tasks.

Set clear goals. To do this right, define the desired action, target audience, and outcome format. Then, clearly communicate your task using action verbs and specifying the response’s length and format.
Provide context. Give the model key terms and concepts related to your topic, including relevant facts and details, and references to pertinent information sources.
Use multi-shot prompting. Show the desired level of detalization using input-output examples and illustrating the expected style.
Be specific. Use precise language without ambiguity and vagueness, adding particular details relevant to your domain.
Experiment and iterate. Test different prompt lengths and formats and experiment with various keywords and structures.
Leverage step-by-step reasoning. Use CoT prompts, guiding the model through the steps required to solve the problem, and asking for a reasoning explanation.

Prompt engineering is one of the AI model optimization techniques. If you’d like to see how it compares to other approaches like Retrieval Augmented Generation (RAG) and fine-tuning, read our article on RAG vs. fine-tuning vs. prompt engineering.