AI Evaluation Pipeline Setup (Onboarding)

AI Evaluation Pipeline Setup (Onboarding)

1. 01

   Define the quality bar for the client's AI use case before any instrumentation

   Agree on measurable criteria such as accuracy thresholds, latency budgets, and cost ceilings per conversation or task, and document them in the statement of work.

2. 02

   Select an evaluation and observability platform that fits the client's data residency and budget

   Compare managed options like Braintrust or Arize against open-source self-hosted choices such as Langfuse, factoring in the client's privacy obligations and the agency's own infrastructure capacity.

3. 03

   Instrument the application with tracing SDKs to capture every LLM call, tool invocation, and retrieval step

   Ensure traces include prompt and response payloads, token counts, latency, and cost per request so that downstream dashboards reflect true production behavior.

4. 04

   Set up automated evaluations using a mix of LLM-as-judge and deterministic checks

   Define scoring rubrics for correctness, tone, and safety, and run them on a golden dataset before launch to establish a baseline for regression detection.

5. 05

   Configure alerts for cost spikes, latency regressions, and quality score drops

   Set thresholds that trigger notifications to the delivery team, and route them to the appropriate channel so issues are addressed before the client notices.

6. 06

   Create a dashboard that the client can access to see live performance metrics

   Share a read-only view showing key indicators like average response time, cost per session, and evaluation scores, which builds trust and demonstrates the value of the observability investment.

7. 07

   Document the evaluation workflow in the client's runbook, including who owns each alert and how to escalate

   Assign a named engineer or analyst as the primary responder for each alert type, and define the escalation path to the account lead for critical failures.