As I’ve been exploring how to build systems around large language models (LLMs), one theme keeps recurring: performance alone isn’t enough. Trust in these systems depends on consistency, safety, and predictability — all of which are hard to guarantee when outputs are probabilistic and context-dependent.
That’s where testing comes in. Compared to traditional software testing, LLM testing is still a developing area with few established best practices. This post summarizes what I’ve learned so far: what “LLM testing” means, the main types of tests, and practical ideas for getting started.
What Is LLM Testing
LLM testing is about defining behavioral expectations — the boundaries of what “acceptable” output looks like — and checking that models stay within those limits as you change prompts, pipelines, or model versions.
Unlike static evaluation, which measures performance on a benchmark, testing introduces pass/fail criteria. It turns LLM behavior into something you can enforce programmatically — similar in spirit to unit tests or regression tests in traditional software.
Because model behavior varies stochastically, the goal isn’t perfect reproducibility but bounded reliability: ensuring the system behaves as expected across representative scenarios.
Types of LLM Tests and Strategies
These categories summarize how researchers and practitioners currently approach LLM testing. They’re not strict stages — more like complementary layers you can combine depending on maturity and goals.
1. Micro / Unit Tests
Test individual prompt–response pairs. Define inputs, expected constraints (structure, keywords, or absence of hallucination), and assert whether the output meets them. Both rigid (exact match) and soft (semantic or similarity-based) checks are useful.
2. Functional / Task-Level Tests
Evaluate realistic workflows — summarization, QA, retrieval-augmented generation — and confirm the model performs consistently across typical examples.
3. Regression Tests
When you change prompts, model versions, or context pipelines, verify that previously passing tests still pass. This helps catch silent regressions introduced by subtle model or prompt drift.
4. Performance / Stress Tests
Measure inference latency, throughput, and cost under different loads or input sizes. These tests expose scaling limits and help plan for production workloads.
5. Safety / Robustness Tests
Probe how the system behaves on adversarial or unexpected inputs. Common focuses include:
- Prompt injection or context leakage
- Hallucination or factual drift
- Bias, toxicity, or unsafe content
- Over-refusal (refusing benign queries)
Recent work explores automated robustness frameworks such as ORFuzz (for over-refusal) and RITFIS (for input perturbation).
6. LLM-as-Judge & Pairwise Evaluation
Use another model — often a stronger or differently tuned LLM — to score or compare outputs. This “LLM-as-judge” method can provide nuanced assessments (clarity, correctness, style) beyond binary assertions.
Good Practices and Common Pitfalls
From what I’ve gathered so far, a few principles seem to recur across projects and research discussions:
- Use diverse datasets — include normal, edge, and adversarial cases.
- Balance strictness and flexibility — overly rigid tests can fail on harmless variation; overly lenient ones miss regressions.
- Revisit tests regularly — expectations evolve as models and prompts change.
- Separate detection from blocking — not all failed tests should halt deployment; some may simply flag issues for review.
- Monitor in production — sample real outputs and feed them into your test pipeline to detect drift.
- Layer different test types — unit, functional, safety, and performance checks together provide better coverage than any single approach.
Reflections
LLM testing is still young — much of the field borrows ideas from traditional software QA, ML model validation, and security fuzzing. What seems to work best so far is incremental progress: start small, automate basic checks, and expand coverage as you learn.
Even partial testing adds value. Over time, a consistent testing loop becomes more than just quality control — it’s a way to understand how your system behaves as it grows more complex.