What is the difference between code-based and LLM-based evals?

Why This Matters for Security Teams

Code-based and LLM-based evals solve different failure modes, so treating them as interchangeable creates blind spots in AI quality gates. Code-based checks are strongest where the expected output can be expressed as rules, patterns, or assertions. LLM-based evals are useful when the question is whether an answer is grounded, helpful, or contextually complete, which is especially important in agentic workflows and review loops. That distinction matters because evaluation quality directly affects release confidence, incident triage, and governance decisions.

For AI risk programmes, the issue is not only correctness but also whether the system behaves safely under prompt injection, tool misuse, or degraded retrieval quality. Guidance from the NIST AI Risk Management Framework and the OWASP Agentic AI Top 10 both support layered evaluation because no single method captures all model and orchestration risks. In practice, many teams discover evaluation gaps only after a model passes a happy-path test suite and then fails on malformed inputs, ambiguous prompts, or business-critical edge cases.

How It Works in Practice

Code-based evals are usually built as deterministic test cases. They compare output against predefined expectations using exact match, regex, schema validation, score thresholds, or unit-test style assertions. These evals are fast, cheap, and repeatable, which makes them ideal for enforcing safety rails such as JSON validity, citation presence, policy keywords, token limits, or prohibited content filters. They also fit well into CI/CD pipelines because failure conditions are explicit and easy to debug.

LLM-based evals use a judge model to score outputs against criteria such as relevance, completeness, coherence, reasoning quality, tone, or groundedness. This is valuable when there is no single right answer, or when the correct answer depends on context that a rule engine cannot capture. Mature programmes usually define a rubric, constrain the judge prompt, and calibrate it against a human-labeled sample set before trusting the scores. That is consistent with the NIST AI 600-1 Generative AI Profile, which encourages measurement, monitoring, and documented evaluation practices for GenAI systems.

• Use code-based evals for syntax, structure, schemas, deterministic policy checks, and regression testing.
• Use LLM-based evals for semantic quality, answer usefulness, summarization quality, and nuanced factuality checks.
• Combine both when outputs must be valid and meaningful, such as customer support, agent planning, or RAG responses.
• Track false positives and false negatives in both layers, because judge drift and brittle rules can each create coverage gaps.

Security teams should also validate the judge itself. An LLM-based evaluator can be manipulated by prompt injection, overly leading rubrics, or contaminated reference answers, so the evaluation pipeline needs its own guardrails and provenance controls. These controls tend to break down when teams rely on one judge model to score every domain because domain-specific language, policy nuance, and adversarial prompts quickly expose weak rubrics.

Common Variations and Edge Cases

Tighter evaluation coverage often increases build time, cost, and maintenance overhead, requiring organisations to balance speed against confidence. That tradeoff becomes more visible as the model portfolio grows and the same eval suite is asked to cover chat, retrieval, tool use, and agentic actions.

There is no universal standard for judge selection yet. Some teams use a stronger frontier model as the evaluator, while others prefer a smaller internal model or a human-in-the-loop review layer for high-risk outputs. The best practice is evolving, but the decision should depend on risk tolerance, data sensitivity, and whether the evaluation itself can be audited. Where outputs affect regulated decisions, a code-only approach is usually too narrow, while an LLM-only approach can be too opaque.

Edge cases matter most when the model output is structurally valid but semantically wrong. For example, a response can pass schema checks while still hallucinating a source, omitting a safety warning, or returning an answer that is technically fluent but operationally misleading. That is why MITRE ATLAS adversarial AI threat matrix and the CSA MAESTRO agentic AI threat modeling framework are useful complements: they remind teams to test how evaluation pipelines hold up under adversarial inputs, not just normal usage.

Related resources from NHI Mgmt Group

• What is the difference between prompt injection and LLM remote code execution?
• What is the difference between tracing production LLM usage and running evals?
• What is the difference between a rules-based secret scanner and a hybrid scanner?
• What is the difference between code scanning and runtime identity monitoring?