Great AI SaaS testing goes beyond unit tests. It continuously validates three things: 1) the product’s facts and payloads are correct (grounding and JSON/action validity), 2) actions are safe and compliant (policy, privacy, fairness), and 3) the system meets performance and cost SLOs in production. Build a layered test strategy: golden evals for content and decisions, contract tests for every integration, safety and fairness checks, and champion–challenger with canaries—observed through dashboards for p95/p99 latency, groundedness, JSON validity, reversal rate, and cost per successful action.
What to test (and why)
- Groundedness and factuality
- Validate that every user‑visible claim is backed by citations; prefer “insufficient evidence” to guessing.
- JSON/action validity
- Ensure all model‑generated actions conform to JSON Schemas and domain standards; block free‑text payloads.
- Domain correctness
- Verify task‑specific outputs (e.g., pricing guardrails, clause extractions, ETA windows) against labeled or rule‑derived truths.
- Safety and policy compliance
- Enforce refusal on disallowed requests; test policy‑as‑code (eligibility, maker‑checker, change windows) and prompt‑injection defenses.
- Fairness and bias
- Monitor subgroup error and intervention rates; ensure exposure diversity and compliance with anti‑discrimination policies.
- Integration resilience
- Contract tests for each API/tool: schema drift, idempotency, retries/backoff, and rollback/compensation.
- Performance and reliability
- p95/p99 end‑to‑end latency by surface; cache hit ratio; router mix; JSON/action validity rate; error budgets.
- Cost and unit economics
- Token/compute per 1k decisions, per‑workflow budgets, and cost per successful action; prevent variant explosions and cache misses.
Test stack: layers and artifacts
- Golden evals (pre‑prod and CI)
- Groundedness: prompts + expected citations and acceptance thresholds.
- JSON validity: tool‑call outputs validated against JSON Schemas.
- Domain tasks: labeled cases (e.g., churn reason codes, clause fields, shipping options) with scoring rules.
- Safety: jailbreak/prompt‑injection, data exfiltration, disallowed topics; refusal and safe‑completion checks.
- Fairness: parity checks across key segments with confidence intervals.
Artifacts: versioned datasets, scoring rubrics, acceptance thresholds, failure exemplars.
- Contract tests (per connector/tool)
- Request/response schema validation, pagination, rate‑limit handling, idempotency, partial‑failure paths, sandbox/prod parity.
- Fixture library and mocks for fast CI; live canary probes for drift.
Artifacts: OpenAPI/GraphQL specs, fixtures, drift classifiers, compensation playbooks.
- Orchestration tests
- Simulation/preview diffs: verify predicted impacts and rollback plans.
- Policy‑as‑code gates: eligibility, approval routes, change windows; SoD/maker‑checker paths.
- Idempotency and rollback: repeated calls produce one effect; failed steps roll back.
Artifacts: policy test matrix, synthetic scenarios, replayable decision logs.
- UX and accessibility tests
- Explain‑why panels render citations and uncertainty; undo works; keyboard/screen‑reader flows pass WCAG checks.
- Latency budgets per surface honored under load.
Artifacts: visual/regression tests, accessibility audits, latency profiles.
- Online experiments and production guards
- Champion–challenger routing with caps; holdouts and ghost offers for causal lift.
- Canary deployments and kill switches; circuit breakers; degrade to suggest‑only on failures.
Artifacts: experiment configs, promotion criteria, rollback runbooks.
Metrics to treat like SLOs
- Quality and trust
- Groundedness/citation coverage %, JSON/action validity %, domain accuracy, refusal correctness, reversal/rollback rate.
- Fairness
- Error and intervention parity (with intervals), exposure diversity, complaint/appeal rate.
- Reliability and performance
- p95/p99 latency, cache hit ratio, router mix, contract test pass rate, DLQ depth.
- Economics
- Token/compute per 1k decisions, unit cost per surface, and cost per successful action (e.g., ticket resolved, upgrade accepted) trending down.
CI/CD and gating policy
- Block releases on:
- Groundedness or JSON validity regressions beyond thresholds.
- Safety/fairness violations or contract test failures.
- Exceeding latency/cost budgets in staging load tests.
- Allow canary with:
- Within‑band metrics and mitigation (kill switch, rollback).
- Monitoring hooks and alerting tied to SLOs and budgets.
Golden eval design tips
- Make evals representative and stable:
- Include hard/edge cases, multilingual/accessibility variants, stale/contradictory sources.
- Score with rubrics, not vibes:
- Exactness for structured tasks; tolerance bands for numeric/interval outputs; rubric scoring for narrative drafts with edit‑distance as a supplemental metric.
- Keep them fresh but versioned:
- Rotate a minority of cases weekly; archive old sets; track model/prompt diffs alongside scores.
Safety and privacy test suite
- Prompt‑injection and egress
- Test adversarial content in docs/URLs; ensure egress filters and source whitelists hold.
- Privacy and data minimization
- Redaction of PII/PHI; tenant isolation; “no training on customer data” paths verified.
- Policy scenarios
- Discounts beyond caps, identity revokes, refunds outside windows; ensure refusal or approval routing.
Cost and latency controls in test
- Router mix budgets
- Assert small‑first routing share; fail tests if large‑model usage exceeds thresholds on known workloads.
- Cache efficacy
- Warm‑cache vs cold‑cache benchmarks; alert on cache regressions.
- Variant discipline
- Cap generations per request; test that prompts/components respect variant limits.
Rollout playbook (60–90 days)
- Weeks 1–2: Baselines
- Define SLOs and budgets; build golden evals (grounding/JSON/safety/domain/fairness); stand up contract tests and canary infra.
- Weeks 3–4: CI gates + observability
- Integrate evals and contract tests into CI; ship dashboards for groundedness, JSON validity, latency, router mix, and cost/action.
- Weeks 5–6: Load + failure drills
- Run load tests; chaos drills for connector failures; validate degrade modes (suggest‑only, queueing, rollbacks).
- Weeks 7–8: Champion–challenger + fairness
- Launch controlled challengers; monitor lift, SLOs, and fairness; promote or roll back using pre‑set criteria.
- Weeks 9–12: Harden + automate
- Auto‑generate fixtures from schemas; scheduled drift checks; weekly “what changed” test narratives; budget alerting and enforcement.
Checklists (copy‑ready)
Quality and safety
- Golden evals: grounding, JSON validity, domain accuracy, safety/refusal, fairness
- Thresholds and rubrics codified; CI blocks on regressions
- Prompt‑injection and egress tests; privacy redaction checks
Integrations and orchestration
- Contract tests (schema, idempotency, retries, drift)
- Policy‑as‑code tests (eligibility, approvals, change windows)
- Simulation/preview and rollback tests
Observability and SLOs
- Dashboards: p95/p99, cache hit, router mix, groundedness, JSON/action validity, reversal rate
- Alerting on SLO and budget breaches; kill switches and canaries
Cost and performance
- Router‑mix budgets; cache efficacy checks; variant caps
- Token/compute meters; cost per successful action by workflow
Governance and audit
- Immutable decision logs; test data lineage and access
- Model/prompt registry with versioned eval results
- Audit export generation tested regularly
Common pitfalls (and how to avoid them)
- Testing only for accuracy, not action safety
- Add JSON validity, policy gates, and rollback tests to first‑class CI checks.
- One‑off manual evals
- Automate with versioned datasets and CI gates; keep a living golden set.
- Ignoring fairness until late
- Add subgroup metrics and exposure constraints from the start.
- Free‑text to production tools
- Enforce schema validation and simulation; refuse on invalid payloads.
- Cost/latency surprises
- Budget tests, router‑mix assertions, cache benchmarks; separate batch from interactive lanes.
Bottom line: Treat AI SaaS testing as an always‑on discipline that validates truth, safety, and economics—not just model quality. With golden evals, contract tests, policy gates, canaries, and SLO‑driven observability, teams ship changes quickly while keeping outputs grounded, actions safe, latency predictable, and costs under control.