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“The demand for AI prompt engineers has increased 300% year-on-year. This is due to the rapid adoption of generative AI technologies such as ChatGPT, DALL-E, and other large-scale language models (LLMs),” said Jaideep Kewalramani, COO, TeamLease Edtech.
“AI-centric companies are also investing in integrating generative AI capabilities into their core business functions, thereby creating further demand,” he added.
Tata Consultancy Services, India's most highly rated IT company, said agile engineering will enable companies to curate AI responses to align with business intent and also reduce costs.
“…interaction with LLM allows you to extract information, summarize text, answer questions, generate codes and classify text,” said Janardhan Santhanam, vice president and chief learning officer at TCS.
But as GenAI applications scale, he warns that prompt engineer may not remain a separate role: “Instead, all stakeholders in the GenAI application lifecycle will need to acquire prompt engineering skills to improve outcomes.”
Accenture and IBM did not respond to ET's queries on the matter.
ETtech Details | Prompt Business Art and Technology
Best results, lowest costs
Prompt engineering is the process of structuring instructions that can be interpreted and understood by a generative AI model. It is the interface between human intent and machine output, and requires a special understanding of each AI model's architecture, training data, tokenization methods, and model parameters.
Key skills required to qualify as a prompt engineer include proficiency in natural language processing, understanding of AI models such as ChatGPT, Google Gemini, and DALL-E, strong analytical and problem-solving skills to create, test, and refine prompts, and often familiarity with programming languages such as Python.
“These skills are crucial for the entire lifecycle of an AI model's instructions: training, fine-tuning, testing, controlling for artifacts and inaccuracies, building safety guardrails, and finally deploying it to production,” said Paramdeep Singh, co-founder of GenAI solutions startup Shorthills AI.
He's quick to dismiss the notion that a prompt engineer's job is simply to write text, pointing out that “that's a misconception.”
For example, in testing, every input and output of the model incurs a cost. A skilled teleprompter engineer can get the best results at the lowest cost, instead of trial and error. Similarly, writing and debugging software code using GenAI tools also requires expertise and knowledge of programming languages.
The cost-effectiveness aspect becomes even more important for large-scale imagery models such as Midjourney, Dall-E, etc., which are expensive and require more computation.
“As the industry moves toward complex multi-modality across text, voice, image and video formats, prompt engineering is emerging as a key differentiating skill set. In fact, we believe we will soon see startups offering prompts as a service,” said Anushree Verma, director analyst, emerging technologies and trends, Gartner.
rapid growth
Industry experts point out that the growth of AI prompt engineering jobs in India has shown a notable increase over the past year.
Sekhar Garitha, CEO of talent platform Foundit, estimates the average annual salary for a job-ready engineer in India to be between Rs 800,000 and Rs 1.3 million. Overall, the company expects India's generative AI job market to grow at a compound annual growth rate of 20% over the next decade.
But compared with the United States, where prop engineering is one of the most sought-after skills, opportunities are still scarce, experts say.
“In the US, starting salaries range from $120,000 to $350,000 per year,” Kewalramani says. “The US market is leading in terms of early adoption and advanced applications of generative AI, creating a growing demand for skilled, agile engineers,” he says.
E42.ai, an AI-powered process automation startup, said that as the model matures, teleprompter engineers will play a key role in integrating it into applications and designing user-friendly interfaces.
For example, the startup built a tool that can generate SWOT reports. “By creating precise prompts, our engineers enabled the AI model to accurately extract relevant information from vast datasets,” said Sanjeev Menon, co-founder of E42.
