Groq Data Handling

Overview

Manage data flowing through Groq's inference API. This skill wires a privacy pipeline around the Groq SDK: sanitize prompts before they are sent, filter responses after they return, redact PII, hash-log an audit trail, and track token usage and cost. Key fact: Groq does not use API data for model training (Groq Privacy Policy).

Prerequisites

• Node.js project with the groq-sdk package installed (npm i groq-sdk).
• A Groq API key exported as GROQ_API_KEY. The SDK reads it automatically from the environment — new Groq() needs no explicit argument. Never hardcode the key; keep it in an untracked .env or your secret manager.
• Node's built-in crypto module (for the audit hash) — no install needed.

Instructions

The pipeline layers in four stages; drop simple add-ons (moderation, cost reporting) on top. Each snippet below is the skeleton — the full, copy-ready code for every stage is in references/implementation.md.

1. Sanitize input — run a PII rule table over every message before it leaves your process, flagging which categories were caught:

   function sanitizeMessages(messages: any[]): { messages: any[]; hadPII: boolean } {
     // apply PII_RULES to each message's content; return redacted copy + flag
   }

2. Wrap the completion call — call safeCompletion(...) instead of the raw groq.chat.completions.create, so input and response both pass the sanitizer.

3. Track usage — trackUsage(model, completion.usage, sessionId) records token counts and estimated cost per call using a per-model price table.

4. Audit — auditedCompletion(...) ties it together and logs a SHA-256 hash of the prompt (never the prompt text) so the audit trail carries no sensitive content.

For content moderation via Llama Guard and a daily cost report, see references/examples.md.

Groq data policy

• Groq does not train on API request/response data.
• Prompts and completions are processed and discarded.
• Groq may temporarily log requests for abuse prevention.
• For enterprise: contact Groq for DPA and SOC 2 compliance details.

Output

• Sanitized messages/responses — text with [EMAIL], [PHONE], [SSN], [CARD], [IP] placeholders swapped in for detected PII, plus a hadPII boolean and a list of redacted categories.
• Usage records — one JSON line per call (type: "groq_usage") with model, token counts, and estimatedCostUsd.
• Audit entries — one JSON line per call (type: "groq_audit") carrying a prompt hash, piiDetected, responseFiltered, and the usage record.
• Cost report — an aggregated object with totalCost, totalTokens, totalCalls, and a per-model breakdown (see the sample in references/examples.md).

Error Handling

Examples

• Full four-stage pipeline (sanitizer, safe wrapper, usage tracker, audited completion) — references/implementation.md.
• Content safety check with Llama Guard and a daily cost report — references/examples.md.

Minimal end-to-end use once the helpers are in place:

const { content, audit } = await auditedCompletion(sessionId, messages);
// content is PII-filtered; audit is a hash-only record safe to persist

Resources

• Groq Privacy Policy
• Groq Pricing
• Llama Guard (content moderation)
• Full implementation · Examples

For enterprise access controls, see the groq-enterprise-rbac skill.