ANNOUNCEMENT

Hexaware Positioned as a Visionary in the 2026 Gartner® Magic Quadrant™ for Custom Software Development Services

Gartner, Magic Quadrant for Custom Software Development Services, By Jaideep Thyagarajan, Ryan McKinney, Nathan Davie, 28 September 2026. Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates. Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Hexaware.

Prompt Engineering

What is Prompt Engineering?

Prompt engineering is the practice of designing effective input prompts to guide how a generative AI model responds. It is a critical part of AI prompt engineering, especially when using large language models for tasks like writing, summarizing, or code generation. Businesses now rely on prompt design to drive the performance of generative AI services.

Why Does Prompt Engineering Matter for Organizations?

The benefits of prompt engineering include higher output quality, fewer model errors, and faster deployment of AI capabilities across business functions. Prompt engineering enables teams to automate workflows, personalize customer interactions, and reduce manual intervention. For teams working with AI in software development, marketing, legal, or customer service, prompt engineering ensures that generative models align with business goals.

How Does Prompt Engineering Work?

Prompt engineering works by refining the structure, tone, and content of the input provided to an AI model. Engineers iteratively test generative AI prompts to identify which formulations produce the most reliable outcomes. This includes adjusting temperature settings, adding context, or using examples. Effective prompt design often involves collaboration between technical and domain experts to ensure the prompt performs well in real-world use cases.

What are the Prompt Engineering Techniques?

Organizations apply a range of prompt engineering techniques to improve model behavior:

Zero-shot prompting, which gives the model a direct task without examples
Few-shot prompting, which provides a few examples to guide response style
Chain-of-thought prompting, where the model is instructed to reason step by step
Role-based prompting, which assigns the model a defined persona or function
Prompt tuning, which optimizes prompts through repeated testing and refinement

What Are Some Prompt Engineering Best Practices?

Teams often follow these prompt engineering best practices to ensure consistency and scale:

Write clear, unambiguous instructions tailored to the model’s capabilities
Use examples where possible to guide structure or tone
Avoid vague or overly broad prompts that may confuse the model
Test prompts across different models and domains
Document successful prompts as reusable prompt engineering use cases

What Does a Prompt Engineer Do?

A prompt engineer is responsible for creating, testing, and refining prompts that drive high-performing AI outputs. This includes understanding how models interpret language, aligning prompts with business objectives, and collaborating with cross-functional teams. Prompt engineers also build prompt libraries and evaluation frameworks to support scaling AI prompt engineering efforts across products and functions.