How the right context improves artificial intelligence performance

Prompts and context: definitions and differences

A prompt is an instruction sent to an AI system. It can be a simple question or a detailed command that includes the objective, format, and criteria for the answer. Prompt engineering combines techniques for drafting and refining these instructions.

Context engineering, also known as context engineering, goes further. It involves selecting, organizing, and updating everything the tool needs to know to perform a task. This includes instructions, relevant history, available data, reference documents, and tools that can be used.

Anthropic, the company behind Claude, describes this practice as a natural evolution of prompt engineering. The distinction becomes relevant when AI needs to perform tasks that go beyond a single response, such as investigating information, consulting files, executing actions, and reviewing results over several stages.

Why context is crucial in different areas

Imagine a marketing team asking an AI to create a publishing schedule. A command can specify the number of posts, tone of voice, and target audience, providing the system with an initial direction.

The result improves when the tool receives additional information, such as previous campaigns, publication performance, product characteristics, commercial calendar, and communication restrictions. This data helps make the suggestions more aligned with the company’s reality.

The same logic applies to other areas. In financial analysis, reports and comparison criteria help interpret indicators. In programming, existing code, project rules, and error logs guide more appropriate changes.

Context reduces reliance on assumptions, although it does not eliminate errors or guarantee correct answers.

How to build an effective context

For those who use AI daily, this change does not mean abandoning prompts. Clear instructions remain important. The next step is to consider what the tool needs to know to perform each task effectively.

Before submitting a task, it is helpful to identify reference materials, explain the objective, indicate limitations, and define how the result will be evaluated.

The ability to formulate good requests remains valuable, but understanding the context of a task can be what distinguishes a generic response from a result that truly serves the purpose.