Custom eval frameworks: Building domain-specific tests

LLMs ace generic benchmarks, then stumble on real work: your data, your tone, your rules. That gap ships bugs, burns trust, and quietly inflates costs.

The fix is not more benchmarks; it is better ones. Domain-specific evaluation turns fuzzy feels-right judgments into clear, repeatable pass or fail. The playbook here is simple enough: build a targeted dataset, craft rubrics that mirror your industry, and wire the whole thing into CI so it blocks bad releases before users ever see them.

Why domain specificity matters

Generic checks miss domain truth. Off-the-shelf metrics like BLEU or ROUGE barely move when the problem is policy answers, legal summaries, or support escalations. You need targeted checks that mirror your use case. Practitioners in the LangChain community show how custom metrics uncover real issues that generic scores hide Reddit.

Context sets the rules; nuance decides correctness. A solid LLM evaluation framework encodes that nuance with precise rubrics and consistent judges. If you want options, the Athina team’s roundup of open-source frameworks is a helpful map Athina. Martin Fowler’s testing catalog backs the core idea: fit-for-purpose tests beat vague, one-size-fits-all suites Fowler.

What “good” means shifts by domain. For a healthcare chatbot, clarity and contraindication safety matter. For RAG support, format and evidence citations matter. Standard suites trail off at the edges; specialized checks fill the gap. Here is what typically matters:

• Faithfulness to source context, not just surface overlap.

• Topic and tone boundaries that match your audience and brand.

• Risk gates for safety, abuse, and data leakage.

Stack fit matters as much as metrics. Many teams need local, self-hosted evals for privacy and control Self‑hosted. Tools like DeepEval make it easy to encode custom judges and automate scoring DeepEval. CI hooks are non-negotiable; align eval runs with your existing pipelines, as covered in Statsig’s overview of automated testing frameworks Statsig.

Designing a targeted evaluation dataset

Start with goals, not models. Tie the dataset to product risks: where things break, who gets hurt, and what costs explode. Pull real examples from logs and support threads. This mirrors the self-testing mindset that Fowler champions: build checks that reflect the actual code and behavior you ship Fowler.

Cover both breadth and depth with diverse, domain-specific cases. Include tricky formats, long contexts, date math, policy edge cases, and ambiguous phrasing. If the app uses RAG, reflect real retrieval noise and distractors. A scan through open-source eval frameworks can help catalyze ideas for these probes Athina.

Then split the dataset. Known truths catch regressions; novel queries expose generalization limits. This split makes blind spots visible and keeps an LLM evaluation framework honest over time.

Use precise, custom rubrics per slice. Judge correctness, clarity, tone, and evidence separately. Community tips from LangChain practitioners offer concrete rubric patterns and judge prompts Reddit. Automate the busywork with DeepEval so reviewers only handle tough edge cases DeepEval.

Practical steps to get moving:

• List must-pass behaviors; map each to a rubric field and a sample count.

• Pull failures from production; label once; reuse often in regression sets.

• Track results in your LLM evaluation framework; ratchet thresholds as the model improves. Statsig’s guides on testing and optimization outline how to fold user feedback into this loop Statsig.

Creating metrics and rubrics that fit your industry

Goals set the bar; domain benchmarks make it measurable. Define correctness and fidelity the way your workflow sees them. A compliance summary needs source-accurate citations; a support answer needs resolvability and tone. Score what users value, not what is easy to compute.

Use expert rubrics with stepwise reasoning. Break answers into criteria: correctness, clarity, tone, safety, and format. G‑Eval style judges and DAG-style decomposition help reduce flukes and encourage consistency Reddit. DeepEval provides structured judges so these criteria become repeatable, not ad hoc DeepEval.

Add context-aware checks for truthfulness and grounding. Run faithfulness tests and needle‑in‑the‑haystack probes that verify the model can retrieve and cite the right facts. For RAG-heavy workflows, the open-source ecosystem has several tools that make these checks turnkey Athina.

Lab scores only go so far. Pair rubric grades with real user signals and sentiment. This is the same spirit as self-testing code and agile feedback loops Fowler; Statsig’s practical guide shows how to close that loop with live traffic and experiments Statsig.

Practical moves:

• Define per‑industry metrics: legal fidelity, medical contraindication safety, support resolution quality.

• Run evals on-prem when data is sensitive; a self-hosted path keeps control where it belongs Self‑hosted.

• Pick a stack your team will actually use. Community threads are great shortcuts for tool selection and gotchas LLMDevs LLMDevs.

Putting domain-specific frameworks into action

The rubric is ready; now ship it. Wire specialized tests into CI/CD so every PR and nightly run gets a green or red light. This is straight out of the self-testing playbook and pairs well with existing automation patterns highlighted by Statsig Fowler Statsig.

Treat each check like a unit test for your LLM evaluation framework. Use custom judges based on one-shot and G‑Eval methods to avoid brittle string comparisons Reddit. Automate assertions with DeepEval or one of the options in the open-source roundups DeepEval Athina.

Then watch it live. Stand up dashboards to track drift, toxicity, and faithfulness over time. Pull signals from real usage and experiments; this pragmatic loop is covered in Statsig’s guide to testing and optimization and reinforced by Fowler’s observability mindset Statsig Fowler.

Close the loop with domain experts who know the edge cases. Codify decision criteria per experiment so reviewers stay consistent; Statsig shared a useful approach to experiment-specific decision frameworks that is worth adapting Statsig. Keep an eye on what other teams favor to control cost and scope without reinventing the wheel LLMDevs LLMDevs.

A practical CI blueprint:

• Pre-merge: smoke tests on must-pass behaviors; fast judges only.

• Nightly: full suite with faithfulness and risk gates; artifact diffs for failures.

• Weekly: human review on a rotating sample; update rubrics and thresholds.

Closing thoughts

Generic benchmarks will not save an LLM product. Domain-specific evaluation will. Build a dataset tied to your risks, encode expert rubrics, automate the checks in CI, then verify it all with live user signals. That loop is how teams ship reliable AI without playing guess-and-check.

More to dig into:

• Martin Fowler on self-testing and observability Fowler

• Open-source evaluation frameworks and RAG tooling Athina

• DeepEval for structured judges and automated metrics DeepEval

• Statsig on automated testing frameworks and practical AI experimentation Statsig Statsig

Hope you find this useful!