LLM Output Validation Guide

Use this guide when model output reaches a user, triggers a tool, generates code, or gets stored for later reuse. The failure mode is simple: the model produces something plausible, and the system trusts it too early.

Assumptions

You know what outputs the workflow is supposed to produce.
Your application can reject, transform, or hold unsafe output.
Anthale policies can run on the output path for the relevant workflow.

Validate the output

Define the allowed output shape

Decide whether the output should be natural language, a structured object, code, a query, or a tool call. You cannot validate a shape you never defined.

Require structured data where the workflow allows it

Use schemas, enums, allowlisted fields, and deterministic parsing for any output that drives automation or writes to another system.

Run Anthale on the output path

Evaluate the response before it reaches a user or downstream system. This is where Anthale can return detect, redact, or block based on the policy for that boundary.

Use sanitized content after a redaction

If Anthale returns redact, keep only the sanitized content in the next step of the workflow. Do not send or store the original output.

Validate side-effecting payloads separately

Treat code, SQL, shell commands, and tool arguments as proposals until your application validates them against allowlists and business rules.

Test with adversarial and malformed cases

Check how the workflow behaves when the model produces hidden instructions, sensitive data, malicious links, or an invalid structured response.

Output validation is not the same as prompt injection protection, but the two often meet at the same boundary. Injection changes what the model tries to do. Output validation decides whether the system will accept it.

What this looks like in code

import json
from os import environ

from anthale import Anthale

client = Anthale(api_key=environ["ANTHALE_API_KEY"])
OUTPUT_POLICY_ID = environ["ANTHALE_OUTPUT_POLICY_ID"]

ALLOWED_ACTIONS = {"answer", "escalate"}

def validate_support_response(model_output_json: str) -> dict:
response = client.organizations.policies.enforce(
policy_identifier=OUTPUT_POLICY_ID,
direction="output",
messages=[{"role": "assistant", "content": model_output_json}],
metadata={"boundary": "final_output", "feature": "support-agent"},
)
if response.action == "block":
raise RuntimeError("Anthale blocked the model output.")

    safe_output = response.redacted_messages[0]["content"] if response.action == "redact" else model_output_json
    payload = json.loads(safe_output)

    if payload["action"] not in ALLOWED_ACTIONS:
        raise RuntimeError("The model returned an unsupported action.")

    if payload["action"] == "escalate" and not payload["ticketId"].startswith("TICKET-"):
        raise RuntimeError("Escalations must reference a valid ticket ID.")

    return payload

import Anthale from "anthale"

const client = new Anthale({ apiKey: process.env.ANTHALE_API_KEY })
const OUTPUT_POLICY_ID = process.env.ANTHALE_OUTPUT_POLICY_ID!

const ALLOWED_ACTIONS = new Set(["answer", "escalate"])

type SupportResponse = {
  action: string
  message: string
  ticketId?: string
}

export async function validateSupportResponse(modelOutputJson: string): Promise<SupportResponse> {
  const response = await client.organizations.policies.enforce(OUTPUT_POLICY_ID, {
    direction: "output",
    messages: [{ role: "assistant", content: modelOutputJson }],
    metadata: { boundary: "final_output", feature: "support-agent" },
  })

  if (response.action === "block") {
    throw new Error("Anthale blocked the model output.")
  }

  const safeOutput = response.action === "redact" ? response.redacted_messages[0].content : modelOutputJson
  const payload = JSON.parse(safeOutput) as SupportResponse

  if (!ALLOWED_ACTIONS.has(payload.action)) {
    throw new Error("The model returned an unsupported action.")
  }

  if (payload.action === "escalate" && !payload.ticketId?.startsWith("TICKET-")) {
    throw new Error("Escalations must reference a valid ticket ID.")
  }

  return payload
}

Anthale decides whether the output can continue, but your application still validates the payload shape and business meaning before another system consumes it.

Common failure patterns

Raw model output is passed directly into a tool, database, or shell.
Anthale returns redact, but the original output is still logged or forwarded elsewhere.
Output checks exist for user-facing text but not for code, queries, or machine-readable payloads.
Teams validate syntax but not business meaning, destination, or entitlement.

Apply this in Anthale

Create a first policy when you want to test output-path enforcement with a working Anthale policy.
See the enforcement API when you need the exact output-direction contract before wiring production traffic.
Request access when you want to review an output validation path with Anthale in place.

Next steps

Continue with Secure Memory and State or Content Moderation.

Documentation

Quick Start

Learn

LLM Output Validation Guide

Assumptions

Validate the output

What this looks like in code

Common failure patterns

Apply this in Anthale

Next steps

​Assumptions

​Validate the output

​What this looks like in code

​Common failure patterns

​Apply this in Anthale

​Related pages

​Next steps

Assumptions

Validate the output

What this looks like in code

Common failure patterns

Apply this in Anthale

Related pages

Next steps