A prompt is the natural language text you pass to generative AI. Prompt engineering is the art of fine-tuning these prompts to better communicate with generative AI.
As Arturo Buzzalino, Chief Innovation Officer, Epicor explains, the value of prompt engineering lies in optimizing how the AI interprets and responds to queries. When done right, it ensures that the AI produces relevant, accurate, and useful outputs.
In other words, prompt engineering is the process of designing the inputs to a generative AI model to create valuable outputs.
“What I find fascinating about this process is that throughout the history of people at work, we have placed a premium on the “answers” that people have,” says Simon Morris, VP of Solution Consulting, ServiceNow. “The world has now changed where large language models contain all of the answers, and the premium is now on the quality of the questions or prompts, that can be created.”
Why do we need prompt engineering?
Arguing that we live in a world of brilliant questions rather than insightful answers, Morris says the value of prompt engineering is that business value can be derived from large language models (LLMs) in a context-specific, use-case-driven way.
The quality of an answer is directly linked to the quality of the prompt and effective prompt engineering is critical to realize value from this emerging technology, believes Morris.
Stefan Leichenauer, VP Engineering, SandboxAQ says that the way you prompt a generative AI model can make a huge difference in the output. This is what makes prompt engineering a critical skill for leveraging the GenAI models effectively.
“In an industrial context, prompt engineering can help align the generative AI’s output to the particular business use case we have in mind, leading to actionable results,” says Leichenauer.
Ramesh Parthasarathy, SVP of Engineering at Freshworks agrees. He says the value of prompt engineering lies in its ability to refine and maximize the performance of AI by addressing nuances in the underlying models.
“These models are trained on vast datasets, so small changes in the prompts can lead to significantly different responses,” says Parthasarathy. He stresses when done right, prompt engineering can help overcome biases, improve data analysis, and tailor AI responses to specific business needs.
Importance of prompt engineering
Leichenauer says prompt engineers specialize in maximizing the utility of a GenAI model. Their role is to fine-tune AI inputs leading to accurate, relevant outcomes.
“In practice, this means AI tools can be applied more effectively, whether it’s generating customer insights, streamlining content creation, or improving operational efficiency,” he explains. “Without this expertise, businesses risk getting outputs that are too vague or misaligned with their goals.”
Similarly, Buzzalino believes businesses leveraging GenAI need prompt engineers to bridge the gap between human intent and machine understanding. He explains that since prompt engineers tailor the interaction with AI models, they help enhance performance by crafting prompts that elicit precise responses. This in turn improves efficiency, reduces errors, and aligns AI outputs with specific business needs.
Looking at it from a technical perspective, Parthasarathy says prompt engineers aim to optimize token usage and consequently costs, while also deriving the most appropriate responses based on the context of the problem.
He says that without the right prompts, AI models may produce suboptimal or even misleading responses, a phenomenon often referred to as “garbage in, garbage out.”
“Prompt engineers can help businesses harness the full potential of GenAI by crafting inputs that align with the company’s goals, improve efficiency, and mitigate risks such as undesirable outputs,” says Parthasarathy.
Prompt Engineering use cases
Prompt engineering can be employed in any process wherein the outputs of a GenAI system need to be optimized, explains Parthasarathy.
It can enhance AI’s ability to perform data analysis, provide personalized recommendations, or address biases in models. One of the most widely used scenarios, he illustrates, is where the GenAI system is being used as a ‘content assistant’ which includes writing text, language translation, drafting emails, and so on.
Leichenauer believes prompt engineering should be used wherever we have GenAI, which opens it for use across all industries.
For instance, if you go to your favorite GenAI chatbot and ask a question without any extra context, you’ll probably get a reasonable answer written out in a few paragraphs. But if you ask the same question while also asking for an executive summary at the top that contains a few key bullets, you’ll get an output that is fit for a senior stakeholder. Or you can ask for expert-level technical details that are needed for implementation. Each of these is an example of prompt engineering tailored to the reader, explains Leichenauer.
Building on this, Buzzalino says prompt engineering is not just crucial in developing conversational AI agents for customer service, but also for generating personalized marketing content, automating complex report generation, and extracting specific insights from large datasets to aid strategic decision-making.
Drilling further, Morris says customer support agents can use prompts to find solutions to customer issues, draft replies to cases, and summarise cases between agents. Similarly, marketing folks can build campaigns, and generate industry messages and perspectives.
What makes a good prompt engineer?
Morris says a good prompt engineer has a mix of both technical and creative skills to design effective prompts. “Most importantly, they possess domain knowledge of the use case that they are trying to enable, which is why it is important that prompt engineers are also great coaches and mentors to regular business users,” says Morris.
Speaking technically, Parthasarathy says a good prompt engineer knows how to expertly guide AI models using smart techniques, like zero-shot and multi-shot prompting. Zero-shot, he explains, works well when you’re asking a model for something new, while multi-shot helps refine responses by giving specific examples.
He says a prompt engineer should also understand the strengths and limits of different models. For instance, prompts that work well for a Claude Sonnet model might not be ideal for a LLaMA model, which is why adapting them is also a key part of the role.
At the same time, a good prompt engineer is aware of risks like prompt hijacking or jailbreaking and makes sure the model is used responsibly and stays protected against these threats.
“On the technical side, balancing accuracy, latency, and cost is crucial,” says Parthasarathy. “You want prompts that are efficient without overloading the system or blowing the budget.”