Article

Prompt Engineering in Tech: What It Is & How To Use It 

Prompt engineering is a new buzzword that has taken the world of AI by storm.

In recent years, the technology industry has experienced a major shift, with large language models (LLMs) becoming central to development workflows. These models have proven to significantly accelerate development cycles, helping developers ship code faster and more efficiently. However, this wave of innovation has sparked debate within the developer community. Some champion the integration of AI assistants, claiming they can increase productivity by 10x, while others argue that reliance on such tools undermines the credibility and creativity of developers.

AI-powered coding assistants are primarily used as integrated development environment (IDE) plugins, but they are also available as standalone tools (like Codeium) or integrated directly into platforms (such as GitHub Copilot). These assistants do more than just assist with coding—they offer features like code completion, refactoring, synthetic data generation, and the creation of unit test cases. At the core of these functionalities is the ability to interact with the assistant through natural language prompts, making prompt engineering an essential skill.

But what exactly is prompt engineering, and why is it becoming so critical? Keep reading to learn more about this emerging field and how it can help you maximize the power of AI tools in your development process.

What is Prompt Engineering?  

Prompt engineering is defined as “the process of designing inputs for generative artificial intelligence models to deliver useful, accurate, and relevant responses.” It has enabled developers and others alike to communicate with powerful Generative AI models by using natural language, pushing the boundaries of their own knowledge to produce features and products faster.  

What is the Method to Prompting? 

There are many techniques that are used within prompt engineering to calculate a desired output. These techniques are a combination of logical and arithmetic formulas used to feed context to the LLM in order to produce the most accurate outcomes. Below are a few popular prompting techniques that are commonly used today: 

Zero-Shot Prompting

This is the most popular form of prompting when it comes to interacting with LLMs. Zero-shot prompting is when the LLM is given a prompt containing instructions from the user that does not contain examples or demonstrations. This technique can be used to test the quality and accuracy of the underlying dataset. It also showcases the astounding ability for these models to provide relevant information across diverse domains 

Zero-Shot Prompt Example:  

Prompt: Classify the sentiment of this sentence into neutral, negative, or positive. Sentence: “I had a very fun vacation to the Bahamas!” 

Output: The sentiment of the text “I had a very fun vacation to the Bahamas!” is positive.  

Chain-of-Thought (CoT)

This enables complex reasoning by providing intermediate reasoning steps. It’s called chain-of-thought because the LLM needs to maintain context of several key points of information within the prompt in order to produce a relevant response. This technique is a great way to observe how LLMs handle multi-step reasoning and adapt to various complex tasks.  

Chain-of-Thought Example:  

Prompt: Ned has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now? 

Output: Ned started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer is 11.  

Decomposed Prompting

Decomposed Prompting (DecomP) offers a modular approach to reaching a solution by breaking down complex tasks into simpler subtasks. This technique breaks down instructions into multiple prompts and can be used to achieve a more accurate output when it comes to complex solutions that involve multiple steps and dependencies. 

DecomP Example:  

Prompt: How many words are in this sentence? 

Output: There are 7 words in this sentence. 

Prompt: Of these 7 words, how many vowels?  

Output: In the sentence “How many words are in this sentence,” there are 12 vowels. 

  • A: 3 
  • E: 5 
  • I: 2 
  • O: 2 
  • U: 0 

These methods involve creating some form of context for the model to focus on. Depending on the size and complexity of the request, it comes down to process of elimination to determine the best method to achieve the best results.  

Developer Productivity Gains 

Before acknowledging the fears of AI taking over the world, we must first acknowledge that having access to these AI code assistants have been known to increase developer productivity as well as give time back to developers to focus on business strategy and reviewing code– not just writing it. This has created a recurring effect on other tech positions like data scientists who may use prompts to clean and train their data model. It can also be inclusive of business analysts that aggregate data to create meaningful business insights, or support engineers that need to query a knowledge base for tracking a known error on an incident.   

In a study by IT Revolution, data was collected from over 5,000 developers across multiple Fortune 100 companies. While developers aren’t the only ones benefiting from these tools, the results from their experiences alone tell a compelling story of increased productivity. For example:

  • Task Completion Rate: Developers completed 26% more tasks on average, allowing dev teams to deliver projects sooner, reducing time-to-market and enhancing their capability to solve even more complex challenges. 
  • Increased Code Volume: Weekly code commits increased by 13.5%, resulting in faster prototyping, more frequent testing, and creation of an environment that is even more agile than before. 
  • Faster Iterations: Code compilation increased by 38.4% which resulted in faster development cycles amongst development teams. 
  • Junior Developer Advantage: Less experienced developers had the largest productivity gains, jumping from 21% to 40%. 

Shifting theSoftware Engineering Experience 

Development is a subset to the larger community of software engineering. When speaking for software engineering as a specialized technical discipline, it’s not about coding, it is about solving problems. With that said, the need for developers who can understand how to architect solutions will increase. 

With many options for LLMs integrated into our development toolbox, we now see that it is more feasible than ever for developers to solve a problem if they understand the building blocks of the solution. Based on analyst predictions from Gartner, 75% of enterprise software engineers will use AI coding assistants by 2028. Additionally, the study emphasized the immediate ROI of AI coding assistants in areas such as cost reduction, value creation, increased retention rates, enhanced developer experience, reduced bugs, and reduced technical debt. 

AI Coding Assistants and Junior Software Engineering  

Although the strides in AI adoption have positively impacted business growth, they can also present challenges to the market for emerging software engineering talent. Without the proper training, it’s only a means to an end– forfeiting the value of learned experiences for code that “just works.”  

Before LLMs rose to popularity over the past ten years, development took a lot of time. Developers drilled every line of code themselves and referred to Stack Overflow, official documentation, and books when needed. Now that less time will be spent on coding, the barrier of entry for software engineering will increase and companies will desire smaller development teams or pods composed of senior level engineers. All low-level tasks and boilerplate code can be handled by AI assistants faster and cheaper than human labor.  

This is proved in a study by Pearson Education and ServiceNow where an was conducted analysis on the impact of AI on junior software engineer skillsets. Their findings led them to conclude that nearly 26% of junior engineering tasks will either be augmented or fully automated by 2027. Within the past decade, the market for software engineering has become substantially competitive, and will become increasingly so with the rise in adoption of AI assistants.   

Finding Balance 

There is a world where software engineers and LLMs live in harmony. Earlier we touched on the developer productivity gains experienced when using LLMs and prompt engineering. LLMs cannot replace software engineers anytime soon. Generative AI is great at handling things like complex computations, creating boilerplate code, providing detailed documentation, and working in a small, confined context. Where LLMs fall short is when it comes to creating contextual understanding at a higher level, creative problem solving, code debugging/error handling, and domain/industry expertise.  

As a result, LLMs and more specifically prompt engineering alone is not going to steer the ship for you, but it will provide some direction. This could lead to the tech market expanding within areas such as enterprise architecture, cybersecurity, and DevOps.

Currently, developers are still in the driver seat. Problem solving requires human intuition, experience, and understanding the big picture. AI assistants are just coming along for the ride!