Summarization Prompts
AI excels at summarization when given clear guidance on length, focus, and audience. Learn to extract exactly the information you need.
The Summarization Challenge
Types of Summaries
Executive Summary
For decision-makers who need the bottom line.
Technical Summary
For experts who need the how, not just the what.
Abstract/Overview
For researchers scanning many documents.
Key Takeaways
For learners who need actionable insights.
Length-Controlled Summaries
Exercise: Multi-Level Summary
Focused Summaries
Structured Summaries
Exercise: Meeting Summary Prompt
Extractive vs Abstractive
Extractive vs abstractive vs hybrid summarization
| Criteria | Extractive | Abstractive | Hybrid |
|---|---|---|---|
| Approach | Pull key sentences directly from the source | Rephrase the main points in new words | Quote key parts and explain the context around them |
| Best for | Preserving exact wording and source fidelity | Concise, readable overviews in plain language | Reports where statistics and claims need exact quotes |
| Example prompt | Extract the 5 most important sentences from this article verbatim. | Summarize the main points in your own words. | Summarize the findings, using direct quotes for statistics and claims. |
Extractive
- Approach
- Pull key sentences directly from the source
- Best for
- Preserving exact wording and source fidelity
- Example prompt
- Extract the 5 most important sentences from this article verbatim.
Abstractive
- Approach
- Rephrase the main points in new words
- Best for
- Concise, readable overviews in plain language
- Example prompt
- Summarize the main points in your own words.
Hybrid
- Approach
- Quote key parts and explain the context around them
- Best for
- Reports where statistics and claims need exact quotes
- Example prompt
- Summarize the findings, using direct quotes for statistics and claims.
Comparative Summaries
Practice: Summary Template
Good summarization prompts specify not just length, but focus, audience, and structure.
Ready for the Next Level?
You now have a strong command of the fundamentals: clear instructions, roles, context, examples, output formats, and chain-of-thought. The next step is learning to engineer prompts rather than just write them: measure their quality with evals and rubrics, automate scoring with LLM-as-judge, use meta-prompting to refine them, get reliable structured outputs, and optimize across quality, cost, and latency.
Take Advanced Prompt Engineering to turn prompting from a craft into a measurable, repeatable discipline.