Summaries lose signal. This prompt extracts structure instead, and it changes what you can actually do with a 100-page document.
The Real Problem With Long Docs
When you hit a token limit, the usual move is to ask for a summary. Summaries sound useful. They’re not. They keep the narrative and throw out the logic.
Think about what actually happens when you summarize a 50-page contract. You get: “This agreement outlines the responsibilities of both parties and includes payment terms, termination clauses, and liability limitations.” That sentence is technically accurate and completely useless. It tells you the document exists. It does not tell you what Party B is required to do by March 15, what triggers the penalty clause, or how the indemnification section connects to the liability cap.
Summaries are written for humans skimming at a conference. Logic maps are built for humans making decisions. Those are two different things, and most people treat them like the same thing.
What you need from a long document is the structure: the key entities, how they connect, and what depends on what. A summary gives you a story. A logic map gives you something you can actually work with.
The token limit is not your enemy here. The approach is. Feed the same document through the right prompt and you stop fighting context windows and start extracting leverage.
The Knowledge Distillation Technique
There’s a prompt pattern that solves this. Instead of asking for a summary, you ask for the document’s “Semantic DNA.” Strip out all the filler, keep only the core entities, and map how they relate.
What does that look like in practice? Say you’re working through a 40-page research paper on AI safety. A summary might tell you it’s “a study examining the risks of misaligned optimization in large language models.” The Semantic DNA approach gives you something different: Entity 1 is the optimization objective. Entity 2 is the reward model. Entity 3 is distributional shift. Then it maps what happens when Entity 1 and Entity 3 interact under real-world conditions. Now you have a scaffold you can reason with, not a paragraph you’ll read once and forget.
You get 10 critical entities and their relationships instead of three paragraphs that say a lot and mean very little.
The other thing this technique does is make chunking actually useful. Most people chunk long documents and feed sections sequentially, but they lose the thread between sections because each chunk gets summarized in isolation. If you run Semantic DNA extraction on each chunk instead, you get structured outputs that stack. Section one gives you entities 1 through 4. Section two adds entities 5 through 8 and shows how they interact with the earlier ones. By the end, you have a coherent map of the whole document, not a pile of disconnected summaries.
Use Cases
- 📄 Legal contracts: map the obligations, parties, and conditions before doing any analysis. You can spot conflicts between clauses in two minutes that would take a human an hour to catch manually.
- Research papers: surface the hypothesis, methodology, and conclusions without the academic padding. Skip 30 pages of literature review and go straight to what the study actually claims and whether the methodology supports it.
- Internal reports: extract the decisions, owners, and dependencies in one pass. Especially useful when a 20-page quarterly report has three buried decisions in appendix B that everyone else on the team missed.
🎯 Prompt of the Day
Copy this directly:
“Extract the ‘Semantic DNA’ of this text. Omit all articles and filler. Provide a logic map of the 10 most critical entities.”
Works best section by section on long documents. Run it on each chunk, then combine the maps. You’ll cover ground that a regular summary would bury.
A few things that make this work better: paste clean text rather than raw PDF exports full of headers and page numbers. Tell the model what you’re going to do with the output, whether that’s drafting a response, building a comparison, or briefing a team. And if the document has domain-specific terminology, add a line asking it to preserve exact terms rather than paraphrase. Paraphrasing kills precision, and precision is the whole point.
You can also layer this prompt. After you get your entity map, follow up with: “Which two entities have the most dependencies? What breaks if either changes?” That second question is where the real insight lives. It forces the model to reason about fragility, not just structure, and that is often the thing you actually needed to know.
Try It Today
Find one doc you’ve been avoiding because it’s too long. A report, a contract, a research paper. Run the prompt on the first section and see what comes back. The difference in output quality is worth the five minutes it takes to test.
Most people never get there because they assume the length is the problem. It is not. A well-structured 100-page document is easier to work with than a poorly structured 10-page one, once you have the right extraction method. Start with one section, see what the map looks like, and build from there. The document that felt impossible to work with last week becomes a tool you can actually use.
The ‘Knowledge Distillation’ Protocol.
by u/Significant-Strike40 in PromptEngineering