AI Regression Testing

Why standard tests aren't enough

Standard unit tests verify deterministic logic. AI features produce non-deterministic output. A model update, prompt change, or embedding version bump can silently degrade quality without any test failing.

What to regression test

1. Model output drift: Same input produces substantially different output after model/prompt changes

   • Baseline: record outputs for a fixed set of inputs
   • On each change: re-run and compare (semantic similarity, not exact match)
   • Alert if similarity drops below threshold

2. Prompt sensitivity: Small prompt changes cause large output changes

   • Test multiple phrasings of the same question
   • Verify consistent answers across phrasings

3. Embedding consistency: Vector search results change after re-indexing

   • Baseline: record top-K results for test queries
   • After re-index: compare overlap (should be >80% for same data)

4. Tool calling accuracy: Agent calls the correct tool with correct arguments

   • Record expected tool call sequences for test scenarios
   • Verify the agent's actual tool calls match

Implementation pattern

def test_invoice_search_regression():
    baseline_results = load_baseline("invoice_search")
    current_results = agent.search("overdue invoices in March")
    overlap = len(set(baseline_results) & set(current_results)) / len(baseline_results)
    assert overlap >= 0.8, f"Search regression: only {overlap:.0%} overlap with baseline"

When to run

• After any model version change
• After any prompt modification
• After re-indexing embeddings
• After changing chunking strategy or retrieval parameters