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Patrick Cornelißen
Patrick Cornelißen

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From prompt engineering to context engineering

#ai #prompting #workflow #productivity

For a long time, "prompt engineering" meant finding the right words. Better instructions, clearer examples, stricter output formats.

That still matters, but it is no longer the whole story. The more useful shift is from prompt engineering to context engineering.

Prompting is only one layer

A good prompt can improve an answer. But many failures do not come from bad wording. They come from missing context.

The model does not know:

  • your codebase conventions
  • the current ticket
  • the relevant API documentation
  • your team's security rules
  • which files changed
  • what "done" means in this workflow

If that context is missing, the model has to guess. Better phrasing will not fix that.

What context engineering means

Context engineering is the practice of deliberately shaping what the AI sees before it acts.

That includes:

  • system instructions
  • project documentation
  • examples of good output
  • relevant source files
  • test results
  • tool output
  • business constraints
  • role-specific requirements

The goal is not to dump everything into the context window. The goal is to provide the smallest useful context that lets the model make the right decision.

A simple example

A weak request:

Review this code.
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A better prompt:

Review this TypeScript code for bugs and readability.
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A context-engineered workflow:

Review the diff for this pull request.
Use our TypeScript conventions from AGENTS.md.
Pay special attention to error handling and tests.
Include only findings that could affect behavior, security or maintainability.
Reference file paths and lines.
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The last version is not just better wording. It tells the model what information matters and what kind of output is useful.

Context beats cleverness

Teams often spend too much time tuning a single prompt and too little time improving the surrounding workflow.

Useful context usually comes from boring places:

  • a clear project README
  • a short architecture note
  • well-named files
  • test output
  • a current ticket description
  • examples of accepted work

AI tools become much stronger when these sources are easy to retrieve.

The risk of too much context

More context is not always better. Huge context dumps can make the model slower, more expensive and less focused.

Good context has shape:

  • include what is relevant
  • exclude stale information
  • prefer source files over summaries when precision matters
  • prefer summaries when the details are not needed
  • keep instructions consistent

The skill is deciding what the model needs for this task, not what might be interesting.

Practical team pattern

One useful pattern is to move repeated instructions out of chat and into versioned files:

  • coding conventions
  • review checklists
  • release note format
  • security rules
  • writing style
  • deployment steps

Then the AI tool can load those instructions when needed. This is more reliable than copying an old prompt from Slack.

Bottom line

Prompt engineering is still useful, but it is only one part of the workflow. The bigger advantage comes from giving AI systems the right context at the right moment.

That is what makes the difference between a clever answer and a useful result.

This article is based on the German original on KIberblick:
https://kiberblick.de/artikel/grundlagen/prompt-engineering-2026/

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