# Many questions about one text

> Ask as many questions as you like about one text; it is read once and each question is answered from that read.

Source: https://opendecisio.com/docs/guides/many-questions/

Most decisions are several questions about the same text: is it urgent, which team, how bad. Asking them one by one makes a model read the text each time; Decisio reads it once and answers every question from that read.

 

[Figure: Read the text once; ask as many questions as you like. Fifty questions about one 3,000-token document take Decisio 31B 1.08 s, Decisio 12B 1.57 s, H2O-Lightning-4B 2.05 s, Jeff-1.0-Large 47.1 s, Quyet-1.0-Large 48.9 s. On Decisio 31B the first question takes 490 ms because it reads the document; each further question on its own takes 32 to 43 ms from the cache.]

 

## TL;DR

 

With a server running ([Get started](https://opendecisio.com/docs/) starts one), this script sends one text with fifty yes/no questions in a single request and prints how many answers came back and the server’s time:

 

```
import json
import urllib.request

state = "I was charged twice for October. Both charges are 49.00 EUR, same day. Please refund one."
topics = ["a refund", "a duplicate charge", "a login problem", "a cancellation", "a late delivery",
          "a wrong invoice", "a price question", "a bug report", "a feature request", "a legal threat"]
templates = ["Does the ticket concern {}?", "Is {} the main issue?", "Would a person need to handle {}?",
             "Is {} mentioned at all?", "Is the customer upset about {}?"]
questions = {
    f"q{i:02d}": {"type": "noul", "instructions": template.format(topic)}
    for i, (template, topic) in enumerate((t, p) for t in templates for p in topics)
}

request = urllib.request.Request(
    "http://127.0.0.1:8000/v1/systemone",
    data=json.dumps({"state": state, "questions": questions}).encode(),
    headers={"Content-Type": "application/json"},
)
with urllib.request.urlopen(request) as response:
    answers = json.load(response)["answers"]
    print("answers:", len(answers))
    print("server ms:", response.headers["x-decisio-server-ms"])
# answers: 50
# server ms: the server's time for the whole request
```

 

Run against the CPU stand-in at v0.11.1 on 2026-10-14.

  

| Word | Meaning |
| --- | --- |
| state | the text the questions are about |
| first read | the server reading a state it has not seen; it takes time that grows with the text’s length |
| prefix cache | where the server keeps what it read, so a later question about the same state starts from it |
| server time | `x-decisio-server-ms`, the time the server took, without the network |

 

## 1. One request with many questions

 

The state is prefilled once per request, and every question reads it from the prefix cache; each question is scored on its own, so a question in a request is answered as if it had been sent alone. On the Qwen base its probabilities are the same; on the Gemma bases the choice is the same and the probabilities agree to within 0.035 (12B) and 0.0073 (31B). A request can carry as many questions as you have: the figure’s measured case is fifty, on a 3,000-token document, in 1.08 s on the 31B.

 

## 2. Or one request for each question

 

A later, different question about a state read before, sent in its own request, reads the state from the prefix cache too: on the 31B that is 32 to 43 ms against 490 ms for the first read of a 3,000-token document. On the Gemma bases this needs the state’s boundary registered, which the server does by default; [Make it fast](https://opendecisio.com/docs/guides/latency/) says how and what it costs.

 

Common mistake

Comparing the time of one question with the time of fifty is not a fair comparison: the first question pays for the read, and the next forty-nine in the same request share it. Compare a request with fifty questions with fifty requests that each read the text again, which is what the two systems at the bottom of the figure do.

 

## What next

 

- [Make it fast](https://opendecisio.com/docs/guides/latency/): what costs time and which settings change it.
- [Long documents and many options](https://opendecisio.com/docs/guides/long-and-wide/): the limits, and what happens near them.
- [How it works](https://opendecisio.com/docs/how-it-works/): one prompt per question, one forward pass, no generated text.