> ## 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 question for RAG systems

> Learn how to explicitly capture the user query so RAG-specific metrics can be computed in Openlayer

Retrieval-Augmented Generation (RAG) systems typically receive a **user query** that drives both
retrieval and generation. Openlayer uses the question to compute metrics such as
**context relevancy** (how well the retrieved context matches the query),
**answer relevancy** (how well the answer addresses the query), and more.

In most cases, you don't need to do anything — Openlayer automatically infers the question
from the first argument of the outermost traced function. Explicit logging is only necessary when:

* the first argument is not the question (e.g. it's a config object or a dict), or
* the question is constructed or transformed inside the pipeline before being used.

## How to log the question

There are two main ways to provide the question to Openlayer:

<Note>
  The question must be a **string** (`str`) containing the user query.
</Note>

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

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

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

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

### 2. Call `log_question` directly

If you prefer not to rely on a kwarg, or if the question is derived at a
different point in the pipeline, you can log it explicitly:

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

@trace()
def generate_answer(raw_input: dict) -> str:
    query = raw_input["text"]
    # Log the user query manually
    log_question(query)

    context = retrieve_context(query)
    return llm_call(query, context)
```