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20/08/2026 6:11 pm
TL;DR: LLM evaluation metrics turn subjective AI output quality into measurable signals, and Braintrust’s guide shows why factuality, relevance, coherence, safety, and task-specific checks are needed to detect regressions, compare variants, and monitor production behaviour systematically. The governance lesson is that AI quality cannot be managed reliably without repeatable measurement, especially when outputs are non-deterministic and context-dependent.
NHIMG editorial — based on content published by Braintrust: LLM evaluation metrics, full guide to LLM evals and key metrics
Questions worth separating out
Q: How should teams choose evaluation metrics for LLM applications?
A: Choose metrics from the failure modes that would matter in production, not from what is easiest to measure.
Q: Why do RAG pipelines need both retrieval metrics and answer-quality tests?
A: Retrieval metrics show whether the system found the right evidence, while answer-quality tests show whether the model used that evidence correctly.
Q: How do you know if an LLM evaluation process is actually useful?
A: It is useful when it changes decisions.
Practitioner guidance
- Define a metric set by failure mode Start with 2-3 measures that map to real operational risk, such as factuality for knowledge responses, relevance for search workflows, and JSON validity for structured outputs.
- Separate retrieval scoring from generation scoring For RAG systems, test context precision, context recall, and faithfulness independently so retrieval defects are not hidden by a polished final answer.
- Version scorers alongside prompts and models Treat scorer changes as controlled releases, because changing the metric changes what the team thinks is improving or degrading.
What's in the full article
Braintrust's full guide covers the operational detail this post intentionally leaves for the source:
- Implementation examples for code-based and LLM-based scorers in Braintrust's autoevals library
- RAG metric recipes for context precision, context recall, faithfulness, and answer correctness
- Practical guidance on sampling online scoring rules and managing evaluation cost in production
- Examples of custom scorer design for coherence, tone, and safety checks
👉 Read Braintrust's full guide to LLM evaluation metrics and implementation examples →
LLM evaluation metrics: what practitioners need to monitor first?
Explore further
20/08/2026 6:46 pm
LLM evaluation is now a governance control, not a testing convenience. Once model output influences customer service, search, summarisation, code generation, or agent decisions, subjective review is not enough. Metrics create a repeatable control surface for quality, change management, and accountability. In that sense, evals sit alongside other assurance disciplines because they tell you whether the system is still behaving within its intended boundary. The practitioner conclusion is simple: if output quality matters, metrics must be treated as operational control evidence.
A question worth separating out:
Q: What is the difference between code-based and LLM-based evals?
A: Code-based evals are deterministic and work best for format, syntax, and exact constraints. LLM-based evals are better for subjective qualities such as coherence, relevance, and nuanced factuality. Mature programmes use both, because code catches hard failures quickly while LLM judges capture meaning that simple rules miss.
👉 Read our full editorial: LLM evaluation metrics are the control layer for AI quality
