LLM Steering

Many sensored detectors are context-dependent: they only redact a candidate when nearby text contains a confirming label. For example, us_ssn requires "SSN" or "Social Security Number" nearby; passport requires "Passport" or "Passport No."

This is great for precision but creates a problem for LLM-generated text: if the model doesn't know which labels a detector expects, it may write the sensitive value without a label, and the detector will silently skip it.

sensored solves this with two APIs that expose context requirements programmatically so you can steer the model before it writes.

listDetectors()

Returns descriptions of all 129 built-in detectors, including their context hints (if any).

import { listDetectors } from "sensored";

const detectors = listDetectors();

for (const d of detectors) {
  if (d.contextHint) {
    console.log(`${d.id} requires labels: ${d.contextHint.labels.join(", ")}`);
  }
}

redactor.describe()

Returns descriptions of only the detectors active in a given redactor's resolved policy. Useful when you've already configured a redactor and want to steer an LLM based on the active rules.

import { createRedactor } from "sensored";

const redactor = createRedactor({
  presets: ["pii"],
  rules: {},
});

const active = redactor.describe();

for (const d of active) {
  if (d.contextHint) {
    console.log(`${d.id}: ${d.contextHint.labels.join(", ")}`);
  }
}

ContextHint shape

interface ContextHint {
  readonly required: boolean;
  readonly labels: readonly string[];
  readonly position: "preceding" | "following" | "both";
  readonly window: { readonly before: number; readonly after: number };
  readonly instructions?: string;
}

• required — Always true for context-dependent detectors.
• labels — Human-readable label strings the detector looks for (e.g. ["SSN", "Social Security Number"]).
• position — Where labels must appear relative to the candidate: preceding, following, or both.
• window — Character window size the detector searches within.
• instructions — Optional free-text steering guidance for detectors with non-standard context rules. When present, prefer these instructions over the labels array.

Steering pattern

A typical steering workflow:

1. Build your redactor with the desired presets/rules.
2. Call redactor.describe() to get active detector descriptions.
3. Filter for detectors that have a contextHint.
4. Inject the label requirements into your LLM system prompt.

import { createRedactor } from "sensored";

const redactor = createRedactor({
  presets: ["pii"],
  rules: {},
});

const hints = redactor
  .describe()
  .filter((d) => d.contextHint)
  .map((d) => ({
    id: d.id,
    labels: d.contextHint!.labels,
    position: d.contextHint!.position,
    instructions: d.contextHint!.instructions,
  }));

const systemPrompt = `You are a helpful assistant. When writing sensitive data,
include one of these labels nearby so the redaction engine can detect it:

${JSON.stringify(hints, null, 2)}`;

Custom detectors

Custom detectors can declare a contextHint in their DetectorDefinition. See Custom Detectors for details.