TL;DR: A 3-iteration prompt forces AI to pack more useful information into the same word count. Same length. Way more signal.
If you’ve ever asked AI to summarize something and got back a paragraph that feels like cotton candy, this is for you. Lots of volume. Zero substance.
You know the output. “This paper explores the relationship between X and Y, ultimately concluding that there are multiple factors to consider.” Cool. Thanks. That tells me nothing I couldn’t have guessed from the title alone.
The problem is that AI defaults to fluency over density. It’s been trained to produce text that reads smoothly, and smooth text tends to be abstract. Abstractions are easy to chain together. Specific facts require compression choices, and compression is harder. So when you ask for a summary without constraints, you get something that sounds complete but carries almost no information. The words are there. The signal is not.
The technique is called Chain of Density. It comes from an academic NLP paper on summarization, and it’s one of the most practical prompting patterns I’ve come across.
How it works
Start with a 100-word summary. Ask the AI to identify 5 missing “entity-dense” facts, things like specific names, numbers, events, and decisions that got left out. Then rewrite the summary to include those facts without adding a single word.
Repeat that loop three times.
Each pass, the AI has to trade filler for substance. Transitions go out. Hedges go out. Real information goes in. By the third iteration, you have a summary that carries dramatically more value in the exact same container.
Here’s what that looks like in practice. The first summary might say: “The study found that certain interventions improved outcomes.” The third summary says: “A 12-week trial across 400 participants showed daily 10-minute sessions reduced anxiety scores by 31%, compared to 8% in the control group.” Same sentence slot. Completely different value.
The “entity-dense” constraint is what drives this. Entities are the specific, concrete details that anchor information: a person’s name, a date, a percentage, a product, a company, a decision made. When the AI is forced to find five missing ones and fit them in without expanding the word count, it has to make real editorial choices. Vague connective tissue gets cut. Specifics replace it.
The third iteration is usually where it clicks. First iteration you see modest improvement. Second, the hedges start disappearing. Third, the summary reads like it was written by someone who actually understood the source material and had limited space to work with.
Where to use it
- 📄 Research papers you need to digest in 2 minutes
- Meeting transcripts you have to brief someone on
- Long email threads that need a clean executive summary
- 📰 Any article you want to extract maximum value from before sharing
Meeting transcripts are probably the highest-leverage use case most people skip. You sit through a 45-minute call, someone asks you to summarize it for a colleague who missed it. A standard summary gives you vibes and general themes. Run it through Chain of Density three times and you get the three decisions made, the two open questions assigned to specific people, and the one blocker that needs to be resolved by Thursday. That’s an actual brief.
For research papers, the technique is especially effective because academic writing is padded by convention. Abstracts are written to be comprehensive, not efficient. Running Chain of Density on an abstract or introduction surfaces the specific methodology, sample size, finding, and implication faster than reading the full paper yourself.
Prompt of the Day
Copy this and paste it before any summarization task:
“Write a 100-word summary. Identify 5 missing ‘Entity-Dense’ facts. Rewrite the summary to include them without increasing length. Repeat 3 times.”
No customization needed. Works on any content.
The reason this prompt works without modification is the specificity of the constraint. “Make it more concise” gives the AI wiggle room. “Include these 5 missing facts without adding a word” does not. Tight constraints force better compression. That’s the whole mechanic, and the prompt delivers it out of the box.
If you want to push it further, ask the AI to flag which phrases it removed and why. That output becomes a useful audit of what counted as filler versus substance, and it helps you build intuition for where AI pads by default.
Give it a shot
Run your next research read through this prompt. Compare the first draft to the third. The difference usually speaks for itself.
If you want more prompts like this every week, you’re already in the right place. Forward this to someone who’s drowning in long documents.
The ‘Chain of Density’ Summarizer.
by u/Significant-Strike40 in PromptEngineering