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

# Lens Overview

> Prompt intelligence powered by downstream user signals

# Nolma Lens

Lens turns user behavior into actionable recommendations. It answers: **are users actually happy with what your agents produce?**

## How it works

```
User interacts with AI output
        |
Your code calls nolma.signal()
        |
Lens tracks acceptance rates,
edit distances, retry rates
        |
After 100+ signals: Claude generates
evidence-backed recommendations
        |
Dashboard shows: "Switch to gpt-4o-mini,
save $43/mo, low quality risk"
```

## What Lens measures

| Metric          | Description                          |
| --------------- | ------------------------------------ |
| Acceptance rate | % of outputs used without changes    |
| Edit rate       | % of outputs the user modified       |
| Edit distance   | How many characters users change     |
| Retry rate      | % of times user regenerated          |
| Abandon rate    | % of times user left without using   |
| Downgrade score | 0-100 readiness to use cheaper model |

## Downgrade readiness score

Calculated weekly per agent:

| Component                   | Weight |
| --------------------------- | ------ |
| Acceptance rate             | 40%    |
| Stability (low retry rate)  | 25%    |
| Focus (low abandon rate)    | 20%    |
| Consistency (low edit rate) | 15%    |

* Score >= 75 → Shadow mode recommended
* Score >= 90 → Safe to switch models

## Getting started

Add 2 lines to your agent code:

```python theme={null}
# After user acts on the output:
await nolma.signal_async(session_id, "accepted")
```

That's all. Lens starts working after 100 signals per agent.