> For the complete documentation index, see [llms.txt](https://primer.ought.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://primer.ought.org/chapters/question-answering/q-and-a-about-short-texts.md).
# Q\&A about short texts
It’s only a small change to support answering questions about short texts (e.g., individual paragraphs):
{% code title="qa.py" %}
```python
from fvalues import F
from ice.recipe import recipe
DEFAULT_CONTEXT = "We're running a hackathon on 9/9/2022 to decompose complex reasoning tasks into subtasks that are easier to automate & evaluate with language models. Our team is currently breaking down reasoning about the quality of evidence in randomized controlled trials into smaller tasks e.g. placebo, intervention adherence rate, blinding procedure, etc."
DEFAULT_QUESTION = "What is happening on 9/9/2022?"
def make_qa_prompt(context: str, question: str) -> str:
return F(
f"""
Background text: "{context}"
Answer the following question about the background text above:
Question: "{question}"
Answer: "
"""
).strip()
async def answer(
context: str = DEFAULT_CONTEXT, question: str = DEFAULT_QUESTION
) -> str:
prompt = make_qa_prompt(context, question)
answer = await recipe.agent().complete(prompt=prompt, stop='"')
return answer
recipe.main(answer)
```
{% endcode %}
You should see a response like this:
```
A hackathon is happening on 9/9/2022.
```
And a trace like this:
## Exercises
1. Instead of answering directly, add “Let’s think step by step.” as a prefix to the answer part of the prompt. This is often referred to as chain-of-thought prompting.
2. After getting the answer, add another step that shows the question and answer to the agent and asks it to improve the answer.
3. Now iterate the improvement step until the answer stops changing or some # of steps is exceeded. Does this work? This is similar to diffusion models which take a noisy image and iteratively refine it until it’s clear and detailed.
Get feedback on exercise solutions
If you want feedback on your exercise solutions, submit them through [this form](https://docs.google.com/forms/d/e/1FAIpQLSdNNHeQAT7GIzn4tdsVYCkrVEPMNaZmBFkZCAJdvTvLzUAnzQ/viewform). We—the team at Ought—are happy to give our quick take on whether you missed any interesting ideas.
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:
```
GET https://primer.ought.org/chapters/question-answering/q-and-a-about-short-texts.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.