Applied AI
Prompt Engineering Is Becoming Context Design
Why reliable enterprise AI depends less on clever wording and more on evidence, permissions, workflow state and evaluation.
Early generative-AI practice focused on finding the perfect phrase. That remains useful for individual work, but enterprise reliability requires a larger design problem: what information reaches the model, where it came from, what the user is allowed to see, what tools are available and how the result will be checked.
This is better understood as context design. The prompt is one component inside a controlled information system.
A strong prompt cannot repair missing evidence
If a model must answer from an internal policy, the policy needs to be retrieved, versioned and placed in context. Asking the model to be accurate does not provide the missing document. Likewise, asking it not to reveal confidential information does not replace access control.
Reliable systems construct context from approved instructions, user identity, relevant sources, workflow state and output constraints. Each component has an owner and a test.
Structure the task contract
Good instructions define the role of the system, the goal, the allowed evidence, the required output and the conditions for abstaining. They separate source material from user requests so retrieved documents cannot silently rewrite the system's rules.
For consequential work, require citations or structured fields that downstream controls can inspect. A risk summary might include the claim, evidence, confidence, unresolved question and recommended human reviewer rather than a single polished paragraph.
- Goal: what outcome is required?
- Evidence: which sources are authoritative?
- Boundary: what must not be inferred?
- Format: what can a reviewer inspect?
- Escalation: when should the system abstain?
Evaluate systems, not favourite prompts
A prompt that works in five demonstrations may fail across languages, document types and ambiguous requests. Build a representative evaluation set from real tasks, including easy cases, edge cases and adversarial instructions. Measure correctness, groundedness, completeness and harmful failure modes.
When performance changes, diagnose the layer: retrieval, instruction, model, tool call or source quality. This is more productive than endlessly rewriting wording without knowing which failure is being treated.