I'm building an internal LLM eval pipeline for prompts and system prompts

Great use case. For an internal eval pipeline for prompts + system prompts, I’d suggest designing it around repeatability, traceability, and paired comparisons rather than just absolute scores.

Core architecture

1) Eval object model

Represent each test as a structured record:

• system_prompt_version
• user_prompt
• expected behavior / rubric
• test category
• input context / documents
• model config: model name, temperature, max tokens, tools on/off
• output
• judge score(s)
• metadata: timestamp, git commit, prompt version, dataset version

This lets you compare:

• system prompt changes
• user prompt changes
• model changes
• tool / context changes

2) Test suite types

Use a mix of:

A. Golden tests

Concrete expected outputs or patterns. Useful for:

• formatting
• schema adherence
• refusal behavior
• extraction tasks

B. Rubric-based tests

Score outputs on criteria like:

• correctness
• completeness
• tone
• safety
• reasoning quality
• instruction following

C. Pairwise tests

Compare:

• old prompt vs new prompt
• system prompt A vs B
• model A vs B

This is often more stable than absolute scoring.

D. Adversarial / edge cases

Include:

• conflicting instructions
• missing context
• prompt injection
• ambiguous requests
• jailbreak attempts
• long context truncation cases

Scoring strategy

Deterministic checks

Use these wherever possible:

• JSON/schema validity
• regex / formatting checks
• keyword/field presence
• exact matches for small tasks
• unit-test style assertions

Model-as-judge

Use an evaluator model for subjective criteria:

• helpfulness
• correctness
• policy compliance
• style/tone

Best practice:

• provide a strict rubric
• evaluate one criterion at a time
• ask for structured output
• use pairwise preference when possible

Human review

Reserve for:

• borderline cases
• high-risk changes
• judge calibration
• spot checks

Prompt eval dimensions

For prompts/system prompts, useful dimensions are:

• Instruction following
• Stability across reruns
• Robustness to adversarial input
• Output format adherence
• Factual accuracy
• Groundedness in context
• Conciseness
• Tone / persona consistency
• Refusal quality
• Tool-use correctness
• Latency / token efficiency

Practical pipeline

A simple workflow:

1. Load prompt version
2. Run fixed test set with controlled parameters
3. Store raw generations
4. Run automatic checks
5. Run LLM judge
6. Aggregate metrics
7. Compare against baseline
8. Fail the build if thresholds regress

Versioning

Treat prompts like code:

• store in git
• version datasets
• pin model versions
• log evaluator version
• snapshot test fixtures

Metrics to track

Useful summary metrics:

• pass rate
• rubric average
• win rate vs baseline
• regression rate by category
• variance across reruns
• refusal accuracy
• schema-valid rate
• tool-call success rate

Important pitfalls

• Don’t rely only on LLM judges
• Don’t use a single broad score
• Keep prompts and eval sets versioned
• Watch for judge bias toward verbose outputs
• Measure variance; some prompts look good once but are unstable
• Avoid overfitting to the eval set

Recommended minimal setup

If you want a lean v1:

• 50–200 curated test cases
• deterministic checks for format-critical tasks
• pairwise comparison vs baseline
• one rubric-based LLM judge
• a dashboard with per-category pass rates
• regression thresholds for release gating

If you want, I can help you with any of these next:

1. a schema for eval cases
2. a Python eval runner skeleton
3. a rubric prompt for LLM-as-judge
4. a folder structure for the pipeline
5. a design for evaluating system prompts specifically