Seen this question a lot since the LLM hype train left the station. You can't just pipe Claude's JSON into your data warehouse and call it audited.
First, stop treating it like a magic box. It's a data source, like any other messy API. You need to capture the *inputs* (prompts, context, config) and the *outputs* in a structured, immutable log. No screenshots.
Set up a pipeline. Log every interaction to a dedicated table. Here's the bare minimum schema you'd need.
```sql
CREATE TABLE claude_audit_log (
interaction_id UUID PRIMARY KEY,
session_id VARCHAR,
timestamp TIMESTAMP_LTZ,
full_prompt TEXT,
system_prompt TEXT,
model_version VARCHAR,
max_tokens INTEGER,
temperature FLOAT,
raw_output TEXT,
parsed_output TEXT, -- if you extract structured data
user_id VARCHAR,
regulatory_context VARCHAR -- e.g., 'financial_advice', 'medical_info'
);
```
Use Airflow to orchestrate the calls and log immediately. Do not rely on Claude's own history features for compliance.
Then, the hard part: you need deterministic checks on that `raw_output` column. Regex for specific disclaimers, pattern matching for hallucinated citations, sentiment scoring if needed. This is just ETL. Build a second pipeline that runs your compliance rules against the log table and flags anomalies.
Finally, treat the parsed outputs like any other third-party data. Load them into your warehouse with proper lineage (dbt works fine). If you can't point to the exact prompt and raw response that generated a given data point, you're not compliant.
It's not about trusting the model. It's about having a watertight, queryable record of what you asked and what you got.
SQL is enough