From prompt to context: why context engineering is replacing prompt engineering

For two years, the key skill in generative AI was prompt engineering: the art of formulating the right query, with the right role, the right format, and the right examples. This skill remains useful, but it is no longer the primary driver. As models progress and tools become more sophisticated, the crucial question has shifted: it's no longer "how do I formulate my request?" but "what do I give the model to read before it responds?" This is what's called context engineering.

The prompt was a symptom

Early users had to learn almost magical formulas ("act like an expert," "reason step by step") because the models were both less capable and completely ignorant of their interlocutor. Every conversation started from scratch. The prompt therefore had to carry everything: the context, the expectations, the style, the constraints.

Modern templates incorporate naturally formulated requests. What they lack is no longer instruction, but information. A template that ignores your industry, your clients, your document templates, and your past decisions will produce generic work, no matter how sophisticated the prompt. The same template, fed with the right elements, produces work that resembles yours.

The four layers of context

In concrete terms, context engineering consists of organizing four layers of information.

The standing instructions. Who you are, what your organization does, your style guidelines, your rules. In the tools available on the market, this takes the form of project instructions, custom preferences, or configuration files. It's the equivalent of a brief given only once to a new employee, not for each task.

Reference documents. The sources the model needs to consult for this task are: the current sales offer, the standard contract, and the study to be used as a basis. Providing the document rather than summarizing it yourself in the prompt makes all the difference: the model cites, verifies, and remains faithful to the source.

Memory. More and more assistants retain enduring information from one session to the next: your ongoing projects, your preferences, your decisions. When managed well, this memory eliminates repetition. When managed poorly, it accumulates obsolete information. Memory curation (reviewing, correcting, purging) is becoming an essential professional skill.

The connectors. The most recent layer: direct access to company systems via protocols like MCP. The context is no longer just what is uploaded, it's what the AI ​​retrieves itself from the CRM, calendar, or document database when it needs it.

What this changes in practice

The focus of effort shifts from conversation to preparation. An hour invested in building a solid project (solid instructions, clean reference documents, relevant connectors) saves dozens of hours of rewriting and editing. Conversely, users who start from scratch with every conversation pay an invisible price: they spend their time re-explaining what the tool could have already known.

This shift also has an organizational consequence: context is shared, but prompts are not. A good prompt remains in a person's history. A well-configured project, a library of reference documents, and a set of team instructions are transmitted, maintained, and improved collectively. The most advanced organizations we support no longer train their teams to "prompt better": they build AI-powered work environments where the context is already in place when the user arrives.

Traps to avoid

The first pitfall: data overload. More context isn't always better. A model buried under forty contradictory documents produces diluted answers. Context engineering is about selection, not accumulation: the right elements, up-to-date, and without duplicates.

The second pitfall: outdated context. A sales offer from last year in the reference documents will cause all generated proposals to cite the old rates. The context must have an owner and an update cycle, like any reference data.

Third pitfall: inadvertent confidentiality. What is put in context circulates in all responses. Sensitive data requires the same discernment as in a shared folder: who has access, for what purposes, and for how long.

Set up your first environment in one hour

No need for a company project to start: an hour is enough to build a first environment in your current tool, and the approach is the same in Claude projects, ChatGPT projects or Google Gems.

First quarter of an hour: standing instructions. Write a page that fits into four sections: who you are and your role; your organization, its offerings, and its clients in five lines; your writing conventions (language, tone, formats, and length); and your taboos (jargon to avoid, commitments never to make, data never to mention). The quality test is simple: if a new colleague were to read this page, would they be able to produce a document that reflects your style?

Second step: five reference documents, not forty. Select the five documents you constantly refer to: the updated offer, the brochure, the standard contract, the latest activity report, and the proposal template. Name them clearly with a date, and delete any intermediate versions. The golden rule: each document must be the definitive version, because an outdated duplicate does more damage than a missing document.

Third step: the comparative test. Take a task you completed last week without context, redo it in the environment, and compare the two results. If the difference isn't immediately obvious, your instructions are too general: fill them in with the missing information and start again. Repeating this cycle two or three times will calibrate the environment in an hour.

Then, a monthly hygiene of fifteen minutes is enough: reread the memory and purge the obsolete, replace the updated documents, adjust the instruction that let a deviation go unaddressed.

The new skill to be developed

Prompt engineering could be learned in half a day. Context engineering is a deeper skill: it combines document management, knowledge of tools, and information governance. It is also a more enduring skill, as it will outlast evolving models. Yesterday's magic formulas are already obsolete; the ability to provide AI with the right working environment will not become obsolete anytime soon.

To learn more: see how the MCP, Enterprise AI USB-C Port et RAG at the heart of AI systems complement this contextual approach.