SmartRouter — One API for All AI Models

Testing LLM applications is fundamentally different from testing traditional software. Outputs are non-deterministic, failures are probabilistic, and 'correctness' is subjective. Here is a battle-tested testing strategy.

The Testing Pyramid for LLM Apps

```

/ Eval Harness \ <- Full e2e: does the app work end-to-end?

/ Prompt Regression \ <- Does a prompt change break behavior?

/ Integration Tests \ <- Does the LLM call + post-processing work?

/ Unit Tests \ <- Does the routing/parsing/formatting work?

```

Level 1: Unit Tests

Test everything that is NOT the LLM call:

• Request formatting and validation
• Response parsing and error handling
• Retry logic and timeout handling
• Provider selection and failover logic

Mock the LLM API response with deterministic fixtures.

Level 2: Integration Tests

Test the LLM integration with a real (but cheap) model:

• Use GPT-4o-mini or Claude Haiku for test runs
• Test a golden set of 20-50 prompts
• Assert on structure (valid JSON, correct schema), not exact content
• Run integration tests in CI, but cache results for 24h to avoid cost

Level 3: Prompt Regression

When you change a prompt, does behavior degrade?

• Maintain a regression test suite of 100+ input/output pairs
• Compare old prompt vs new prompt on the same inputs
• Use an LLM as a judge: ask it to rate both outputs on relevance, accuracy, tone
• Flag regressions where the new prompt scores >10% lower

Level 4: Evaluation Harness

The gold standard:

• Use eval frameworks (LangSmith, Braintrust, or custom)
• Define metrics: accuracy, latency, cost, toxicity, hallucination rate
• Run evals weekly or on every deployment
• Track metrics over time in a dashboard

The Multi-Provider Testing Problem

Testing gets harder when you use multiple LLM providers. Each provider has different behavior, latency profiles, and failure modes. SmartRouter helps by providing a consistent interface so your tests work across all providers: https://smartrouter.online

Try SmartRouter free — 50+ models, one OpenAI-compatible API. Auto-routing, failover, cost optimization.

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