# Compressing Content — Prompt & Context Engineering

Source: https://www.skillbyai.com/en/prompt-context-engineering/s-compress

> Keep the sentences that matter for this question.

## Extractive and abstractive compression

When good content does not fit, **compress** it. **Extractive** compression keeps only the sentences relevant to the query (cheap, exact wording preserved, easy to cite). **Abstractive** compression asks a model to summarise (shorter, but it costs a call and can drop or distort details). Compress per chunk with the question in mind, keep identifiers, numbers and dates exact, and keep a link to the original so answers can still be cited and checked. Measure whether compression hurts accuracy before relying on it.

## Keeping query-relevant sentences, run

I ran this with plain Python 3 (standard library only); the data is made-up example data. A six-sentence policy is cut to the three sentences that mention refunds or returns, from 253 to 155 characters. Real systems would match on embeddings or a model, not a fixed word list.

```python
doc = ("Our company was founded in 2009. Refunds are issued within 14 days of receiving the item. "
       "We have offices in three cities. Items must be unused to qualify for a refund. "
       "Our newsletter is sent monthly. Shipping costs for returns are paid by the customer.")
query = {"refund", "refunds", "returns", "return"}
sentences = [s.strip() + "." for s in doc.split(".") if s.strip()]
kept = [s for s in sentences if query & set(w.strip(",.").lower() for w in s.split())]
print("sentences:", len(sentences), "-> kept:", len(kept))
print("chars:", len(doc), "->", len(" ".join(kept)))
for s in kept:
    print(" -", s)
```

Output:

```
sentences: 6 -> kept: 3
chars: 253 -> 155
 - Refunds are issued within 14 days of receiving the item.
 - Items must be unused to qualify for a refund.
 - Shipping costs for returns are paid by the customer.
```

## Never paraphrase numbers

If you summarise with a model, instruct it to copy amounts, dates and IDs verbatim and spot-check that it did.

**Quiz:** What is an advantage of extractive compression?

- [ ] It needs a second large model
- [ ] It always produces the shortest text
- [x] Original wording is preserved, so it stays exact and citable
- [ ] It removes the need for retrieval

*Answer:* Original wording is preserved, so it stays exact and citable. Extractive keeps exact sentences; abstractive rewrites them.