> For the complete documentation index, see [llms.txt](https://docs.bcbi.brown.edu/codiac-for-health/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bcbi.brown.edu/codiac-for-health/computing/python/large-language-models-llms.md).

# Large Language Models (LLMs)

## Overview

Unstructured clinical text accounts for over 80% of health data. Traditional natural language processing (NLP) methods lack contextual flexibility and domain generalization. Large Language Models (LLMs) enable advanced capabilities: summarization, classification, translation, reasoning.

## Ollama

Ollama is an open-source framework for managing, running, and serving large language models locally on host hardware.

### Prompt Engineering at Scale

* Standardizes prompt templates and variables
* Runs multiple prompts iteratively for evaluation or comparison
* Logs inputs/outputs for reproducibility and auditability

### Input

* Imports both structured data (like spreadsheets/CSVs) and standardized healthcare files (like FHIR patient records)
* Cleans and extracts raw narrative text from unstructured sources, such as messy clinical notes
* Clinical tables and free text can be combined in prompts.

### Output

* LLM responses parsed and stored programmatically.
* Results may be written to data frames or databases for downstream use.


---

# 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://docs.bcbi.brown.edu/codiac-for-health/computing/python/large-language-models-llms.md?ask=<question>&goal=<endgoal>
```

`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.