The Evolution of Prompt Engineering to Context Design in 2026

The work of the “prompt engineer” hasn’t become obsolete, but it must evolve into a context designer for AI agents.

One of the hottest trends we expect to see in 2026 is the use of more elaborate structures in the instructions given to AI-driven models to solve complex problems involving process automation.

In the first year following the public release of the most widely adopted generative AI tools, an industry debate emerged around the new job roles created by this technology. One of the most intriguing roles was that of the “prompt engineer”, an expert in creating instructions to optimize the performance of large language models (LLMs). This has indeed become a booming profession. However, as the comprehension capabilities of chatbots have evolved, the role is shifting toward that of a context architect.

Differences between a prompt engineer and a context designer

We believe that throughout 2026 the role of the prompt engineer will become firmly established, but from a new perspective: as a key enabler helping AI agents better understand context and efficiently automate the tasks they are designed to perform. While traditional prompt engineering has focused on crafting a well-designed instruction to elicit a single response, context engineering treats AI-driven models as dynamic resources that must be structured and enriched to truly harness their potential in the productive economy.

There are subtle but notable differences between prompt engineering and context engineering. In traditional prompt engineering, the user typically prepared an immediate instruction such as: “Create a corporate presentation”, “Summarize this text”, or “Organize these ideas”. The goal was to define the tone, the role the software should play, and the objective – among other factors – in order to obtain a more or less expected result.

By contrast, context design moves beyond the “static prompt” by developing a dynamic information ecosystem. Rather than simply setting a tone, it involves orchestrating a flow of data from multiple sources that updates as the interaction unfolds. This ensures the model’s context window is not just a snapshot, but an evolving state that adapts to user preferences, historical messages, and changing topical nuances.

With this shift in focus, agentic AI becomes more precise, delivers more personalized results, and also becomes more efficient by leveraging data, history, and other connected tools at any given moment. This is, without a doubt, one of the trends identified in the Data, Analytics & AI Trends 2026 report prepared by Orbitae, the SDG Group division driving AI innovation.

The evolution of the prompt engineer over time

The concept of the “prompt engineer” began to be widely discussed in 2022. The following year, more advanced techniques emerged, such as chain-of-thought prompting, which breaks down complex problems into more logical, step-by-step sequences. By 2024, memory and history features were introduced, allowing AI agents to advance even further. Then in 2025, the true potential of context linking began to emerge, enabling AI systems to generate better answers.

We have therefore reached a new starting point in the AI revolution, where context will be king. This represents a broader discipline that, as we will see in 2026, will allow businesses to overcome the limitations of relying on a single static instruction. We therefore anticipate a rise in automated code-generation systems, which will play a fundamental role in accelerating the development of new products. Most importantly – and rooted in a basic principle that is sometimes overlooked – this new paradigm will allow professionals without advanced programming knowledge to generate highly sophisticated, data- and AI-driven solutions.

As we move from isolated tasks to the design of more complex workflows, the potential of AI for businesses is becoming increasingly significant. Until now, many professionals have used generative AI simply to upload a PDF and ask the model to produce a summary and some conclusions. For businesses, however, this is far too simplistic and has minimal impact on medium- to long-term strategy.

The real goal is for users to instruct intelligent software to analyze a report and take a holistic view of relevant company information – such as revenue over a specific period – in order to generate more precise and actionable recommendations.