Contextual prompting
Fundamentals
Level 1 · FoundationsGive the model relevant background before the task, instead of asking directly.
Contextual prompting means explicitly providing background information — who this is for, what's already known, what circumstances matter — before stating the task itself, rather than asking the question directly and hoping the model infers what it needs. It's arguably the single most foundational habit in prompting: most disappointing answers come from missing context, not from a wrong technique.
When to use it
Use it whenever the task depends on information the model can't infer on its own — who the audience is, what's already been tried, what constraints already exist. Almost every real-world prompt benefits from at least a little explicit context.
When to avoid it
Avoid over-loading context that isn't actually relevant to the task — irrelevant background can distract the model as much as missing context can hurt it. If the task is genuinely self-contained, a short context section is enough.
Advantages
- Directly reduces generic, one-size-fits-all answers
- Makes the task easier to state simply, because the context does the work of narrowing it down
- The single technique with the best effort-to-benefit ratio for beginners
Disadvantages
- Too much irrelevant context can dilute the actual task
- Doesn't help if the context provided is itself wrong or outdated
Examples
Context: I'm writing documentation for a REST API used by external developers who are not familiar with our internal terminology. Most of them are integrating payments for the first time. Task: Explain what a webhook is and why it matters for this integration.
Pairs well with
Try it
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Relevant background — audience, circumstances, what's already known.
What you want to achieve.
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- Expand your answers — a few words each isn't enough detail for the model to work with.
- Add background context so the model understands the situation.