> 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/long-texts.md).

# Long Texts

Ultimately we’d like for language models to help us make sense of more information than we can read and understand ourselves. As a small step in that direction, let’s make a recipe that answers questions about research papers.

To do so, we’ll:

1. [Parse and load papers](/chapters/long-texts/loading-paper-text.md)
2. [Find the relevant parts of papers](/chapters/long-texts/finding-relevant-paragraphs.md)
3. [Answer given those relevant parts](/chapters/long-texts/answering-given-paragraphs.md)

In this tutorial, we’ll take a simple approach to step 2: We’ll classify for each paragraph whether it answers the question or not. We then take the paragraphs with the highest probability of answering the question and ask a model to answer the question given those paragraphs, reusing [the question-answering recipe](#answering-the-question-given-the-top-paragraphs-with-subrecipes).