Is Prompt Engineering Becoming Obsolete?

In the early days of large language models (LLMs) like GPT-3, prompt engineering emerged as the most popular and sought after skill. By carefully crafting instructions, users could elicit intelligent responses from models with limited memory and no real-world grounding. 

This led to the development of vibe-coding among developers that enabled the use of natural language and accurate code suggestions from LLMs.

But the moment an AI system needs to integrate with a company’s proprietary API or align with a team’s specific architectural patterns, its limitations quickly become apparent.

In this regard, context engineering is shifting the way in which developers take on the role of curating, managing, and optimising the knowledge accessible to AI assistants. Instead of depending solely on pre-trained knowledge, they become architects of the AI’s understanding, shaping the information environment to ensure more accurate and relevant outcomes.

But as LLMs have evolved, growing more powerful, memory-rich, and integrable with tools and data, a deeper transformation has taken hold: the rise of context engineering. 

Furthermore, prompt engineering initially gained traction alongside early LLMs with limited context windows. These models required tight, optimised instructions to perform well. Techniques like few-shot prompting, chain-of-thought reasoning, and role-play became popular methods to guide the model’s behaviour. 

However, with the advent of advanced LLMs like GPT-4 Turbo and Claude 3, capable of handling over 100,000 tokens and integrating tools, a shift is underway. 

Adding to this, Kapil Joshi, CEO, IT staffing at Quess Corp told AIM, “Prompt engineering is evolving beyond a standalone role into a core skill within broader AI interaction disciplines like GenAI UX, augmented analytics, and data product design.” 

Why Context Engineering?

Context engineering focuses on three core pillars: environmental setup, tool orchestration, and workflow design. This means giving the AI access to curated persistent knowledge, real-time validated data, APIs and databases, and integrating it within real business processes with human oversight.

As context windows have expanded, the need for “instruction trickery” has diminished. Retrieval-augmented generation (RAG) systems now let models pull in up-to-date facts from external sources. Agentic frameworks allow LLMs to act in software environments, access tools, and reason through tasks — not just chat.

In contrast, context engineering is gaining importance because it allows for persistent knowledge retention, seamless tool use, dynamic information access, and workflow automation. Instead of relying solely on training data, engineers now embed live data feeds, integrate tools, and build multi-layered systems for validation and execution.

Why this Change?

According to Quess Corp, with only 1 qualified GenAI engineer available for every 10 open roles and hiring timelines twice as long compared to traditional ML roles, companies are turning to agile staffing solutions, including contract and part-time roles to quickly operationalise GenAI capabilities.

In this regard Iain Brown, head of data science at analytics firm SAS told AIM that “It’s accurate that many prompt engineering roles are being trialled as contract or part-time positions.” This reflects a broader trend. Companies are cautiously exploring GenAI without overcommitting, given the rapid pace of change and regulatory ambiguity. 

“That said, I’ve seen increasing internal upskilling, where roles like business analysts or marketers are taking on prompt design as part of their existing job, especially in finance and regulated industries,” he added.

Prompt engineering is absolutely real, but it is more a transitional role than a permanent fixture, said Brown, adding that it’s a bridge between current GenAI capabilities and future interaction models. Over time, it will likely evolve into something broader, like AI interaction design or agentic system orchestration. 

Hiring Trends

This evolution is already visible in hiring trends. Neeti Sharma, CEO of TeamLease Digital, a staffing and recruiting firm, said that prompt engineering has become the default skill needed by any company now. “Most CTOs prefer to check a candidates’ prompt library as against the lines of codes they have written. The skills now include advanced methods like prompt chaining and using external knowledge sources. As AI grows, prompt engineering will mix language skills, data logic and subject knowledge.”

When asked about the companies hiring at present, Sharma said that most companies that hire prompt engineers are either AI-led startups, GCCs and large IT services companies.

They get hired for roles such as AI engineer, LLM specialist or GenAI developer with prompt work as a key skill. Companies want prompt engineers who can write and improve prompts, integrate with APIs, fine tune LLMs or SLMs and build AI assistants.

Real-world use cases are also validating the evolution of prompt work into broader systems thinking. Brown points to a case in fraud detection, where prompt engineering is used to generate synthetic fraud scenarios for simulation environments. This helps train models on rare, high-risk events.

In marketing, he notes that “teams use prompt chains to auto-generate campaign assets, customer summaries, and analytics reports, significantly reducing time to insight.” 

Similarly, Sharma shares an EdTech example, where a company built an AI assistant to help students solve doubts in Math and Science. “They used LLMs and prompt engineering to provide clear, accurate responses that match the school curriculum and grade level. As a result, students got answers 65% faster and user engagement went up by 30%.”

For businesses, the payoff is clear: less time spent tweaking prompts, improved accuracy through reusable context, and greater scalability. For AI practitioners, the skillset is shifting, from linguistic precision to systems architecture. The new must-have competencies include working with vector databases, building RAG pipelines, integrating APIs, and deploying human-in-the-loop feedback loops.

Ultimately, prompt engineering hasn’t disappeared, it’s been absorbed into a broader, more strategic discipline. 

As Brown succinctly puts it: “The long-term value lies in combining prompts with domain expertise, ethical considerations, and integration into business workflows.”

If prompt engineering was about giving perfect directions, context engineering is about equipping your AI with GPS, real-time data, and the lay of the land.

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