Most people get frustrated when an LLM gives them a summary that’s either too long or misses the juicy details. I’ve been experimenting with a technique I call Layered Compaction, and it’s a game-changer for getting high-signal, low-noise output.

Instead of just asking for a "detailed summary," you force the model into a five-step loop within a single prompt:

1. Identify Missing Intel: Look at the current draft and find 3-5 crucial facts or entities it skipped.
2. Fuse and Shrink: Rewrite the summary to include those missing pieces, but—here’s the kicker—keep the word count exactly the same as the previous version.
3. Repeat: Do this 4 or 5 times.

By the final iteration, the AI is forced to use incredibly dense, fusion-style sentences. You end up with a paragraph where every single word is doing heavy lifting. I used this on a 20-page whitepaper yesterday, and the 200-word result was more informative than the original executive summary.

Pro-tip: This works best with models that have a strong grasp of latent space (like Gemini 1.5 Pro or GPT-4o). Smaller models tend to "hallucinate" or break the word count constraint by the third round.