> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openlayer.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Log context for RAG systems

> Learn how to capture retrieved context so RAG-specific metrics can be computed in Openlayer

Retrieval-Augmented Generation (RAG) systems depend on a **retriever** that fetches
context documents before passing them to an LLM.
To best evaluate RAG quality, Openlayer needs to know *what context was retrieved* for each request.

Once context is logged, you can use tests that leverage metrics such as **context recall** (did the retriever surface the right information?),
**context relevancy** (how relevant was the retrieved content to the query?), and more.

## How to log context

There are two main ways to provide context to Openlayer:

<Note>
  The context must be passed as a **list of strings** (`List[str]`). Each string
  should represent a retrieved chunk/document.
</Note>

### 1. Use `@trace` with `context_kwarg`

If your function receives the retrieved context as a keyword argument, you can
tell Openlayer which argument contains it.

```python theme={null}
from openlayer.lib import trace

@trace(context_kwarg="context")
def generate_answer(query: str, context: list[str]) -> str:
    return llm_call(query, context)
```

### 2. Call `log_context` directly

If you do not want to rely on a kwarg, or if you get the context at
different points in the pipeline, you can log it explicitly:

from openlayer.lib import trace, log\_context

```python theme={null}
@trace()
def generate_answer(query: str) -> str:
context = retrieve_context(query)
    # Log the retrieved context manually
    log_context(context)

    return llm_call(query, context)
```