Prompting & Context Engineering
Better AI results come from better task design: the right context, evidence, examples and constraints — not magic incantations.
Prompting is becoming context engineering
Early prompt advice often focused on special phrases or elaborate role-play. Those techniques can sometimes change style, but reliable work comes from something more ordinary: giving the model the right goal, information, examples, constraints and tools.
A reusable task brief
Goal: What should be accomplished? Audience: Who is the result for? Context: What does the model need to know? Evidence: Which documents/data/sources should it use? Constraints: Length, tone, exclusions, deadline, policies. Output: Exact format or schema. Checks: List assumptions, uncertainties and items needing verification.
Use examples when style or structure matters
If you need a particular format, one or two good examples can be more useful than a paragraph of adjectives. Examples show the model what “good” means in your context.
Separate instructions from source material
Use headings or clear delimiters so the model can distinguish what it should do from the document, email or webpage it is analyzing. This also helps reduce accidental instruction-following from untrusted content.
Ground factual work
For research, policy, legal, scientific or company-specific questions, provide the source documents or use a system with retrieval. Ask the model to cite the evidence it used and to state when the source does not answer a point.
Ask for checkable reasoning, not performative certainty
For a difficult decision, request a concise rationale, assumptions, calculations, counterarguments and confidence limits. You need an output you can audit — not a promise that the model “thought deeply.”
Prompt patterns that are genuinely useful
Transform
“Rewrite this for a non-technical audience. Preserve every factual claim; do not add new facts.”
Extract
“Return only fields supported by the document. Use null when the source is silent.”
Compare
“Use these criteria. Cite evidence for each difference. Do not invent a winner.”
Critique
“Find the strongest weaknesses, missing evidence and assumptions in this draft before suggesting revisions.”
When the first answer is weak
Do not restart with a vague “try again.” Tell the model exactly what failed: missing evidence, wrong level of detail, weak structure, unsupported assumptions, tone, or a violated constraint. Iterative feedback is usually more effective than stacking more prompt tricks.
Prompt injection is not solved by better wording
When an AI system reads untrusted external content and has tool access, prompt injection becomes a systems-security problem. The fix is layered: trusted instruction hierarchy, data/instruction separation, permission limits, tool validation, approvals and monitoring.