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AI Performance Audit

Benchmark quality, latency, and cost for RAG and copilot workflows

Overview

Performance issues show up as slow responses, high cost per request, and inconsistent answers that teams cannot reproduce.

This self-assessment focuses on measurable performance across quality, grounding, latency, cost, and governance.

Who should complete it:

Scoring Rubric

0: Not in place

1: Partially in place. Ad hoc, inconsistent, or undocumented

2: Mostly in place. Measured and repeatable for the core workflow

3: Fully in place. Standardized, measured, and audit-defensible

SECTION A

Measurement Design and Baselines

Max Score: 18 points
The workflow has defined quality metrics. Accuracy alone is not the only measure.
A representative evaluation set exists. It is versioned and refreshed on a schedule.
Baselines exist for latency, throughput, and cost per successful outcome.
Metrics are segmented by user group, data domain, and scenario type.
Quality targets are tied to business impact. Error tolerance is explicit.
A single dashboard shows quality, latency, and cost in one view.
SECTION B

Retrieval and Grounding Performance

Max Score: 18 points
Retrieval quality is measured with hit rate, relevance, and coverage metrics.
Citations are enforced for knowledge answers. Missing citations count as failures.
Chunking strategy and metadata are documented and tested.
Permissioning is validated. Users cannot retrieve content outside their access.
Freshness controls exist for time sensitive content. Stale answers are detected.
Adversarial tests cover prompt injection and retrieval poisoning patterns.
SECTION C

Model Behavior and Safety

Max Score: 18 points
Refusal behavior is tested for disallowed requests and low evidence situations.
Tool calling is constrained with allowlists and parameter validation.
Prompt templates avoid hidden instructions and reduce ambiguity in tool steps.
Temperature and decoding settings are tuned and recorded per workflow.
Sensitive content handling is tested. Redaction and masking behave as intended.
Human review exists for high impact actions, approvals, or customer facing outputs.
SECTION D

Latency and Throughput Engineering

Max Score: 18 points
End to end latency is broken down by stage. Largest contributors are known.
Caching exists where safe. Cache invalidation rules are documented.
Batching and parallelism are used for retrieval and tool calls where appropriate.
Timeouts and fallbacks exist to keep user experience stable under load.
Performance tests cover peak usage. Load profiles reflect real adoption patterns.
Scaling strategy is defined for model endpoints, vector search, and downstream services.
SECTION E

Cost Efficiency and Spend Control

Max Score: 18 points
Cost per transaction is measured and tied to business value.
Token usage is monitored by prompt version, user group, and feature flag.
Context is minimized. Retrieval limits and summarization policies exist.
Vendor pricing is reviewed quarterly. Alternatives and negotiation levers are tracked.
Guardrails prevent runaway tool loops and repeated calls.
A cost budget exists with alerts and an owner who takes action.
SECTION F

Continuous Evaluation and Governance

Max Score: 18 points
Every change runs automated evaluation. Results gate deployment.
Regression failures create tickets with owners and SLA expectations.
Model and prompt versions are traceable to outputs and incidents.
Audit logs support investigations, customer questions, and compliance evidence.
A governance forum reviews performance, risk, and roadmap monthly.
A documented improvement backlog exists with expected impact per item.

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Your AI Performance Score

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Score Breakdown by Section

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