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General Best Practices for Cline or Roo Code with AI API Gateway

Review & governance best practices

Whether using Cline or Roo Code with the AI API Gateway, use these shared review and governance best practices:

Always validate

Before merge or deployment:

  • Code review
  • Security scan
  • Unit tests
  • Integration tests (if applicable)

Never expose sensitive data

Do NOT include in prompts:

  • API keys
  • Private credentials
  • Internal URLs
  • Customer data
  • Production logs containing PII 

Use placeholders instead:

process.env.API_KEY

Work iteratively

Avoid: “Build the entire system.”

Instead, do this:

  1. Design architecture.
  2. Implement core module.
  3. Add validation.
  4. Add tests.
  5. Optimize.

Incremental development reduces cost and increases quality.

Recommended operational workflow

For Complex Tasks, use this workflow:

  1. Start in Plan Mode.
  2. Review architecture.
  3. Approve approach.
  4. Implement.
  5. Review diff.
  6. Run tests.
  7. Refine with targeted prompts.
  8. Commit only after validation.

Telemetry & observability best practices

Enable structured logging

  • Track structured logs that include at least:
    • Request IDs
    • Timestamp
    • User or team identifier
    • Model used
    • Token usage and cost
  • Ensure logs are compliant with your data retention and privacy policies.

Why: Structured logs help debug errors, analyze cost patterns, and support audit trails.

Monitor both success and errors

  • Track successful completions as well as errors, retries, and fallback events.
  • Correlate spans across services using shared trace IDs.

Why: This gives a full view of system health and helps diagnose patterns rather than isolated failures.

Define alerting thresholds

  • Establish alerting on anomalies such as:
    • Spike in errors
    • Latency increase
    • Unusual jump in token usage or cost

Why: Early detection of issues minimizes impact and prevents runaway costs.

Integrate with observability platforms

  • Use OpenTelemetry or provider SDKs (e.g., Datadog) to collect metrics and traces across your LLM stack.
  • Capture:
    • Latency distributions
    • Error rates
    • Provider fallback usage
    • Token cost per endpoint / per user

Why: Centralized metrics make it easier to detect anomalies and optimize model selection and performance.

Security best practices

Secure your deployment and surrounding infrastructure by applying the following:

1. Protect API Keys & Credentials

  • Store all secrets in a secure vault or secret manager; never check them into version control (e.g., Git).
  • Rotate keys regularly and enforce proper lifecycle management.

2. Use Strong Network Security

  • Enforce TLS for all network traffic, including between your app and gateway.
  • Use network policies, firewalls, and segmentation to limit access.

3. Implement Access Controls

  • Enforce role-based access control (RBAC) for who can invoke or view telemetry.
  • Track identity at the user/team level and tie logs to specific entities for auditability

4. Apply API Request Guardrails

  • Use rate limits, quotas, and budgets by user/team to mitigate abuse or accidental cost spikes.
  • Enforce model restrictions (allow/deny lists) for production environments. 

5. Validate Input and Output

  • Sanitize inputs to guard against prompt injection or malformed queries.
  • Apply output filters to prevent leakage of sensitive data.

6. Logging & Audit Trails

  • Maintain tamper-resistant audit logs of all LLM requests and key system events.
  • Archive logs according to retention policies and ensure they support forensic analysis.

7. Incident Response & Monitoring

  • Define an incident response plan for security events.
  • Ensure telemetry and logs feed into alerting and investigation workflows.

8. Consider Local Hosting for Sensitive Workloads

  • If data sensitivity or compliance demands it, host models entirely on-premise or in your private infrastructure

Final guidance

Cline is strongest when trusted with structured, autonomous implementation.

Roo Code is strongest when used deliberately with control over planning, provider selection, and token management.

Both tools:

  • Require clear requirements.
  • Require human review.
  • Require cost awareness.
  • Reward structured workflows.

Used correctly, they significantly increase development velocity while maintaining engineering standards.

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