Define the job before you compress the text
A text summary is useful only in relation to a reader and a decision. Before you use an AI model, write down who will read the result, what they need to do next, which details are non-negotiable, and how much time they have. A student may need concepts and definitions. An executive may need decisions, owners, deadlines, and risk. A developer may need structured fields and stable labels.
Use a layered request when one length is not enough: a one-sentence overview, five key points, and a fuller brief with evidence. This pattern lets readers stop at the level they need without forcing the model to choose a single compression ratio for everyone.
Use a repeatable text summary workflow
- Clean the source. Remove duplicated headers, footers, navigation, legal boilerplate, and obvious OCR noise while preserving headings and paragraph boundaries.
- Map the structure. List sections, claims, examples, dates, people, and decisions before rewriting them.
- Set the contract. Specify audience, length, format, tone, exclusions, and whether the model may infer anything.
- Generate in layers. Ask for the overview first, then key points, evidence, open questions, and actions.
- Verify high-risk details. Compare names, numbers, quotations, legal obligations, medical claims, and deadlines with the source.
Choose the right summary type
| Summary type | Best for | Must retain |
|---|---|---|
| Executive summary | Decisions and leadership review | Context, options, risks, recommendation, next action |
| Study summary | Learning and recall | Definitions, relationships, examples, likely questions |
| Article summary | Fast reading | Thesis, support, evidence, limitations, conclusion |
| Meeting summary | Operational follow-through | Decisions, owners, deadlines, blockers, unresolved questions |
| Decision summary | Audit trail | Problem, alternatives, criteria, rationale, dissent, review date |
Control accuracy with explicit instructions
Tell the model to stay inside the supplied source, separate facts from inference, and mark missing information rather than filling gaps. When the source has page numbers, section names, or timestamps, request references for important claims. A citation does not prove the summary is correct, but it makes verification faster and exposes unsupported statements.
Useful prompt rule: “Do not add background knowledge. When the source is unclear or incomplete, label the point as uncertain.”
Review the output in three passes
First, check fidelity: does the summary preserve the source’s thesis, sequence, and level of certainty? Second, check coverage: did it omit a major counterargument, condition, exception, or decision? Third, check usability: can the intended reader understand the result without reopening the source immediately? Tighten wording only after those checks.
Next steps
Use the AI summary prompt library for reusable instructions, choose the PDF workflow when extraction is part of the problem, or move to the text summary API guide when the process must be automated.