A context window contains the information a model can consider during one request. More context is not automatically better: relevance, currency, consistency and structure matter more than volume.
What the concept actually means
Context includes system instructions, user requests, conversation history, documents and tool results. Long inputs can displace important passages or reduce quality through conflicting instructions.
Why it matters in the enterprise
Enterprises should treat context as a curated work package: which source governs, what period is relevant, which version is approved and what information may leave the system?
A controlled method
The CTPM practice framework for controllable AI applications uses seven stages: understand the task, clarify context and data, apply AI deliberately, review professionally, handle deviations, approve accountably and document transfer. It is a transparent working framework, not a certification.
- Define task and impact
- Clarify data, context and permissions
- Review against domain criteria
- Control deviations, approval and evidence
CTPM practice example
CTPM practice example: An architecture review includes only current target-state, interface and quality documents. Historical variants are labelled and queried separately so the model does not mix obsolete decisions.
Quality and test criteria
The following criteria make quality observable for this use case:
- Context sources are named and versioned.
- Relevant passages are prioritised over filler.
- Contradictions are handled before output.
- Confidential material follows data classification.
Risks and common misconceptions
Risks include lost-in-the-middle effects, stale conversation state, prompt injection in documents and unnoticed truncation by technical limits.
Example transfer artefact
Transfer artefact: a context checklist covering source priority, version, validity, confidentiality and maximum scope.