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

# Handling false positives

> Reduce noise in surfaced findings and tune review precision.

VectorLint gives you two complementary levers for controlling review precision. Used together, they let you calibrate how aggressively VectorLint surfaces findings across your project.

* **`CONFIDENCE_THRESHOLD`**: controls how strictly raw model candidates are filtered before surfacing them. A global setting that applies to every review.
* **Strictness**: controls how strongly check rules penalize errors when scoring. Set per rule in frontmatter; see [Strictness](/strictness).

Understanding when to reach for each one is the key to a low-noise, high-signal workflow.

## How the two levers differ

`CONFIDENCE_THRESHOLD` operates at the **filtering stage**. It determines whether a candidate violation gets surfaced at all. Lower it and more candidates pass through. Raise it and only the highest-confidence findings appear.

Strictness operates at the **scoring stage**. It determines how heavily a violation is penalized once it's already been surfaced. Higher strictness means a given error density produces a lower score and triggers violations more readily.

They're solving different problems. `CONFIDENCE_THRESHOLD` reduces noise from the model's judgment. Strictness controls how demanding your quality bar is for a given rule.

## When to tune CONFIDENCE\_THRESHOLD

The default of `0.75` is a reasonable starting point. Adjust it when the balance between findings and noise isn't working for your team.

```bash theme={null}
CONFIDENCE_THRESHOLD=0.75
```

| Value               | Effect                                       | When to use                                             |
| ------------------- | -------------------------------------------- | ------------------------------------------------------- |
| Lower (e.g. `0.5`)  | More findings, higher recall, more noise     | Early-stage rule development, finding gaps in coverage  |
| Default (`0.75`)    | Balanced precision and recall                | Most production workflows                               |
| Higher (e.g. `0.9`) | Fewer findings, higher precision, less noise | CI gates, customer-facing content, high-trust workflows |

Set this in `~/.vectorlint/config.toml` for a global default, or in a project `.env` file to override it for a specific project.

<Tip>
  When writing a new rule, temporarily lower `CONFIDENCE_THRESHOLD` to see everything the model flags. Once you've validated the rule's coverage, raise it back to filter out low-confidence candidates.
</Tip>

## Tuning for CI environments

In CI, false positives block merges. A finding that a writer might reasonably dismiss becomes a pipeline failure that needs explaining. Two adjustments help:

**Raise `CONFIDENCE_THRESHOLD` in CI.** Set it higher in your CI environment's `.env` than in local development. This means only the highest-confidence findings block a merge. Lower-confidence candidates still get caught locally where a writer can review them in context.

```bash theme={null}
# .env in CI environment
CONFIDENCE_THRESHOLD=0.85
```

**Use strict patterns only on production-bound content.** Gate CI checks on the directories that actually ship, not on drafts or work-in-progress:

```ini theme={null}
# Only these files block CI
[content/docs/**/*.md]
RunRules=TechDocs

# These files are checked locally but don't gate CI
[content/drafts/**/*.md]
RunRules=
```

## A practical starting point for teams

If you're rolling VectorLint out across a team for the first time, start permissive and tighten over time. A workflow that generates too many findings on day one loses the team's trust before it earns it.

1. Start with `CONFIDENCE_THRESHOLD=0.75` and `standard` strictness
2. Run against your existing content library and review the findings as a team
3. Raise strictness on your highest-stakes rules first
4. Raise `CONFIDENCE_THRESHOLD` once your rules are stable and reviewed

## Next steps

* [LLM Providers](/llm-providers): set `CONFIDENCE_THRESHOLD` in your config
* [Strictness](/strictness): set strictness per rule in frontmatter
* [CI Integration](/ci-integration): gate merges on content quality