A production Claude feature needs the same engineering disciplines as any API-backed system: identity, authorization, input handling, privacy, rate limits, timeouts, telemetry, rollback, and incident response. Model-specific risks add prompt injection, unreliable output, tool misuse, and ungrounded claims.
Evaluation is the bridge between a compelling demo and a dependable feature. Build a representative dataset, define what correct and safe behavior looks like, run it before releases, inspect failures, and monitor drift after deployment.
| Release claim | Evidence to collect | Owner |
|---|---|---|
| It answers accurately | Representative and adversarial evaluation cases with expected behavior. | Product and domain owner |
| It acts safely | Access controls, tool-policy tests, confirmation paths, and redacted traces. | Engineering and security |
| It can be operated | Alerts, budget signals, feature flag, fallback, and rollback playbook. | On-call and product owner |
Map every boundary: user input, uploaded files, retrieved documents, model request, tool call, downstream service, and user-visible response. Each boundary needs a policy about what data can cross it and what validation occurs next.
Prompt injection is untrusted content attempting to change model behavior. Delimit external text, reduce tool privileges, validate actions in code, and test adversarial documents.
An evaluation set represents the work users actually ask for. Include successful cases, ambiguous cases, missing-evidence cases, attempts to bypass policy, malformed inputs, and tool failures. Label expected answer, evidence, allowed action, and escalation result.
Score dimensions separately: factual support, task completion, schema validity, safety, user usefulness, latency, and cost.
A useful trace records a correlation ID, model configuration, redacted input metadata, retrieved source IDs, tool calls, tool results, validation outcomes, latency, usage, and final state. Do not log secrets or entire sensitive documents by default.
Alert on error rate, validation failure, tool denials, timeout, cost anomaly, retrieval miss, and human escalation rate.
Release gradually with a feature flag, internal users, a limited cohort, and a fallback path. Keep a disable switch that removes model actions without breaking the rest of the product. Review incidents for root cause: source quality, prompt ambiguity, authorization defect, tool reliability, or evaluation gap.
Version prompts, schemas, tool definitions, and evaluation datasets. A production change should be reproducible.
Write down the normal, ambiguous, missing-evidence, and malicious-input cases before rollout. A product team can then talk about failures using examples instead of impressions from a demo.
Use a feature flag for consequential behavior and define the signal that turns it off. The rollback decision should be understood before the first customer uses the feature.
A test case records more than the ideal text answer: it captures evidence, permitted action, and the expected escalation path.
{
"id": "refund-missing-evidence-01",
"user_question": "Refund this order now",
"retrieved_source_ids": [],
"expected_status": "needs_review",
"allowed_tool_calls": ["get_order_status"],
"forbidden_tool_calls": ["issue_refund"]
}A kill switch can disable model-initiated writes while leaving read-only assistance available.
FEATURES = {"claude_write_actions": False}
def may_execute_write(user_confirmed: bool) -> bool:
return FEATURES["claude_write_actions"] and user_confirmed
print(may_execute_write(True))FalseA small score record avoids collapsing correctness and safety into one vague pass/fail label.
case_result = {"answer_supported": True, "safe_action": True, "latency_ms": 620}
print(case_result["answer_supported"] and case_result["safe_action"])TrueThe gate is a reminder that a new model feature should not enable itself merely because a request succeeded.
def may_enable_write_actions(eval_passed: bool, alerting_ready: bool) -> bool:
return eval_passed and alerting_ready
print(may_enable_write_actions(True, False))FalseA launch-planning lab
0 of 2 completed
Before behavior-changing releases and regularly against production-like examples; frequency should match the risk and change rate.
No. Application identity, authorization, validation, and operational controls remain required.
Explore 500+ free tutorials across 20+ languages and frameworks.