Lesson 349 of 2116
Synthesis Vs Summary: The Move That Separates Analysts From Aggregators
LLMs default to summarization. Research demands synthesis. Here's how to prompt for the harder, more valuable thing.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1What the two words actually mean
- 2synthesis
- 3summary
- 4argument mapping
Concept cluster
Terms to connect while reading
Section 1
What the two words actually mean
Compare the options
| Summary | Synthesis |
|---|---|
| Compresses one source | Combines many sources |
| Preserves structure of the original | Imposes a new structure across originals |
| Answers: what did it say? | Answers: what does the field think, and why do they disagree? |
| Can be done by Ctrl-F | Requires understanding the field's shape |
LLMs default to summarization because it's linguistically easier. Synthesis requires holding multiple arguments in tension. You have to ask for synthesis explicitly — and give the model a synthesis structure to fill.
The synthesis prompt pattern
- 1Give the model 3 to 10 sources, not just one
- 2Ask for the DISAGREEMENTS, not the agreements
- 3Ask for shared hidden assumptions — this is the most useful output
- 4Demand a quotable synthesis sentence — forces real integration
Key terms in this lesson
The big idea: summary is what ChatGPT does by default; synthesis is what you pay it to do. Prompt for the harder move, explicitly, every time.
End-of-lesson quiz
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