Absolutely. A good eval plan for this kind of LLM product usually has three layers:

1. Automated checks for the majority of outputs
2. Human review for edge cases and ambiguous failures
3. Ongoing monitoring in production to catch regressions and drift

Below is a practical template you can adapt.

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1) Start with the product risk profile

Before defining metrics, decide what can go wrong.

Common risk categories

• Correctness: factual errors, wrong calculations, bad reasoning
• Policy/compliance: unsafe, disallowed, privacy leaks, legal issues
• Tone/style: rude, overly verbose, inconsistent with brand
• Completeness: missing required fields or steps
• Grounding: unsupported claims when the product should use provided context
• Tool use: bad function calls, malformed JSON, wrong API parameters
• User harm: advice causing financial, medical, or safety harm

Classify output severity

Use severity levels to decide what gets auto-checked vs human-reviewed:

• Sev 0: harmless style issues
• Sev 1: minor quality problems
• Sev 2: user-visible correctness issues
• Sev 3: high-risk issues, policy violations, harmful advice
• Sev 4: critical safety/legal/security issues

A useful rule:

• Automate everything that is stable, measurable, and low-risk
• Send to humans anything high-impact, ambiguous, or rare

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2) Define evaluation dimensions

Pick a small set of dimensions that matter for your product.

Example dimensions

Core quality

• Task success
• Accuracy / factuality
• Instruction following
• Completeness
• Conciseness / verbosity control

Safety and compliance

• Policy adherence
• PII handling
• Harmful content avoidance
• Refusal quality for disallowed requests

Product-specific

• Schema validity for structured outputs
• Tool-call correctness
• Citation quality / grounding
• Brand voice
• Localization quality

For each dimension, define:

• What “good” means
• How it is measured
• Whether it is automated, human-reviewed, or both
• The failure threshold

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3) Split evals into automated vs human review

Best candidates for automation

These are usually deterministic or machine-checkable:

• JSON / schema validity
• Required field presence
• Regex / formatting checks
• Tool-call argument validation
• Exact-match or fuzzy-match against known answers
• Citation presence and formatting
• Policy keyword or classifier checks
• PII detection
• Length limits
• Language detection
• Toxicity classifiers
• Unit tests for business rules

Best candidates for human review

These are subjective or high-stakes:

• Factual correctness in open-ended answers
• Reasoning quality
• Helpfulness / completeness
• Borderline safety cases
• Tone and empathy
• Appropriate refusal behavior
• Groundedness when evidence is nuanced
• Edge cases outside the training/eval set

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4) Use a tiered review flow

A practical design is:

Tier A: Automated pass/fail

Run every output through checks like:

• schema validation
• safety filters
• policy classifiers
• known-answer scoring
• citation/grounding heuristics
• tool-call validation

If it fails a hard rule, mark as fail immediately.

Tier B: Uncertainty-based human review

Route outputs to humans if:

• automated checks conflict
• confidence is low
• output falls near decision thresholds
• prompt contains rare or risky intent
• the model self-reports uncertainty, if you use that signal
• the output is from a new prompt type or new model version

Tier C: Sampled human audit

Even for outputs that pass automation, sample a small percentage for human review to catch blind spots.

A common pattern:

• Review 100% of failures
• Review all high-risk categories
• Review 1–5% of routine successes
• Review all new use cases during launch
• Review all outputs from model changes during canary period

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5) Build an eval dataset strategy

You need several dataset slices.

A. Golden set

A stable set of representative examples with expected outputs or rubric-based judgments. Use this for:

• regression testing
• model comparisons
• release gating

B. Edge-case set

Examples designed to break the system:

• adversarial prompts
• ambiguous instructions
• conflicting requirements
• malformed inputs
• rare user intents
• policy boundary cases

C. High-risk set

Focus on scenarios with biggest user or business impact:

• medical, legal, financial
• privacy-sensitive cases
• security-related requests
• regulated content
• tool-action requests with real consequences

D. Production sample set

A rolling sample of real traffic, anonymized if necessary. Use it to discover:

• unknown unknowns
• prompt distribution shift
• recurring failure patterns

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6) Define scoring rubrics for humans

To make human review reliable, use a rubric rather than freeform judgment.

Example rubric

For each dimension:

• 0 = unacceptable
• 1 = poor
• 2 = acceptable
• 3 = good
• 4 = excellent

Or simpler:

• Pass / Fail / Needs escalation

Review instructions should include

• what to check
• what counts as a failure
• examples of borderline cases
• how to handle uncertainty
• when to escalate to a subject matter expert

Measure reviewer quality

To keep human evals trustworthy:

• use overlap on a subset of samples
• measure inter-rater agreement
• periodically calibrate reviewers
• include known-answer “gold” items in the review queue

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7) Define release gates

Set explicit criteria for shipping.

Example release gates

A new model version ships only if:

• schema validity ≥ 99.5%
• disallowed content rate = 0 on high-risk set
• task success improves or stays within tolerance
• factual error rate does not regress more than X%
• no critical failures in human-reviewed edge cases
• no degradation in top business metrics

Use “must-pass” and “nice-to-have” buckets

• Must-pass: safety, compliance, schema correctness, tool reliability
• Nice-to-have: style, verbosity, minor helpfulness gains

This avoids over-optimizing subjective metrics at the expense of safety.

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8) Include regression testing and versioning

Every prompt, model, retrieval index, tool schema, and policy update can change behavior.

Track versions of:

• model
• system prompt
• developer prompt
• tools
• retrieval corpus
• post-processing rules
• safety classifiers

For each release:

• run the golden and edge-case suites
• compare against baseline
• flag regressions by category
• keep a changelog of failures and fixes

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9) Use targeted error analysis

After each eval cycle, group failures by type:

• hallucination
• missed constraint
• wrong format
• unsafe answer
• incorrect refusal
• poor citation
• tool misuse

Then identify:

• which are automatable
• which need new test cases
• which need prompt/model changes
• which need policy changes
• which need human escalation rules

This turns evals into a feedback loop instead of just a scorecard.

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10) Add production monitoring

Even great offline evals miss real-world behavior.

Monitor:

• failure rates
• escalation rates
• user corrections / edits
• abandonment
• re-asks
• complaint rates
• safety-filter triggers
• tool error rates
• drift in prompt distribution
• performance by segment

Set alerts for spikes and regressions.

A useful pattern:

• automated dashboards for daily monitoring
• weekly human audit of sampled conversations
• monthly deep-dive on top failure modes

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11) Suggested operating model

Here’s a simple, effective setup:

During development

• Run automated checks on every candidate
• Human review all failures and edge-case samples
• Iterate on prompts/rules/tests

During release

• Canary new versions on a small traffic slice
• Review all canary outputs that hit risk thresholds
• Compare against current version

In production

• Auto-check all outputs
• Route risky/uncertain outputs to human review
• Sample routine outputs for audit
• Feed failures back into the eval set

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12) A concrete example architecture

You could implement something like:

1. Input classification

   • detect topic, risk level, language, intent

2. Automated validators

   • schema validator
   • policy classifier
   • grounding/citation check
   • business-rule checks

3. Routing

   • if hard fail → block or escalate
   • if uncertain / high risk → human review
   • else → release

4. Human review UI

   • show prompt, output, retrieved evidence, tool traces
   • collect rubric scores and failure tags

5. Dashboard

   • aggregate metrics
   • trend failures by version and category
   • track reviewer agreement

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13) Example metrics table

Dimension	Method	Threshold	Owner
Schema validity	Automated	≥ 99.5%	Eng
Policy violations	Automated + human audit	0 critical	Safety
Task success	Human rubric	≥ baseline	Product
Factuality	Human + spot checks	no regression	DS/PM
Tool-call validity	Automated	≥ 99%	Eng
Escalation accuracy	Human review	improve over time	Ops
User satisfaction	Production metric	positive trend	Product

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14) Practical recommendation: decision matrix

Use this rule of thumb:

Auto-check if:

• the requirement is explicit
• the output format is structured
• a deterministic validator exists
• the cost of a false positive/negative is low

Human-review if:

• the output is open-ended
• correctness is subjective
• the category is rare
• mistakes are high-impact
• the model confidence is low
• the automated signals disagree

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15) Minimal starting plan

If you want something lightweight to begin with:

1. Create a golden set of 100–300 examples
2. Add an edge-case set of 50–100 risky examples
3. Build automated checks for format, policy, and known business rules
4. Define a human rubric for accuracy/helpfulness/safety
5. Review:
   • 100% of automated failures
   • 100% of high-risk examples
   • 5% of routine passes
6. Track failures by category and version
7. Use release gates to block regressions
8. Add production sampling and monitoring

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If you want, I can turn this into:

1. a one-page eval plan template,
2. a metric/rubric spreadsheet, or
3. a workflow diagram for automated + human review routing.