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name: ai-prompt-regression-testing description: 'Prompt engineering regression test matrix register: baseline outputs, semantic drift thresholds, judge evaluations, and golden dataset.' category: engineering risk: safe source: self source_type: self date_added: "2026-10-01" author: Ranjeet2063 tags: [ai, prompt-engineering, testing, evaluation, llm, benchmarking] tools: [] source_repo: Ranjeet2063/agentic-awesome-skills

AI Prompt Regression Test Matrix

What it is: Tracks regression baselines, evaluation rubrics, and automated judge verdicts to prevent output degradation across prompt revisions.

Overview

Provides a standardized, auditable framework and data model for AI Prompt Regression Test Matrix operations across distributed engineering and decentralized application systems.

When to Use This Skill

• When formalizing architectural contracts, security invariants, or operational limits for AI Prompt Regression Test Matrix.
• When cross-functional review is required between protocol developers, smart contract auditors, and AI engineering agents.
• When generating reproducible CSV, SQL DDL, JSON Schema, and Notion property registers for tracking compliance.

How It Works

1. Define the parameters, thresholds, and identity bindings required for the target operational register.
2. Select appropriate boundary enforcement values from validated enum select sets.
3. Export standardized artifacts (CSV table, SQL DDL, JSON Schema) to integrate into validation CI pipelines.

Field Reference

#	Field Name	Type	SQL Type	JSON Schema Type	Notion Property Type	Example Value
1	Prompt TestCase ID	id	SERIAL PRIMARY KEY	integer	Text	PTEST-001
2	Prompt Identifier	text	VARCHAR(64)	string	Text	soroban_code_refactor_v2
3	Target LLM Model Family	select	VARCHAR(64)	string	Select	Claude 3.5 Sonnet
4	Evaluation Metric	select	VARCHAR(64)	string	Select	AST Code Correctness
5	Semantic Drift Threshold	number	NUMERIC(5,2)	number	Number	0.05
6	Golden Baseline Match %	number	NUMERIC(5,2)	number	Number	98.50
7	Judge Model Evaluator	text	VARCHAR(64)	string	Text	Gemini 1.5 Pro
8	Zero-Shot Reasoning Verified	select	VARCHAR(16)	string	Select	Yes
9	Latency Bound Seconds	number	NUMERIC(6,2)	number	Number	3.20
10	Test Suite Verdict	select	VARCHAR(32)	string	Select	Passed
11	Benchmarking Date	date	DATE	string, format: date	Date	2026-10-01

Select Options

Target LLM Model Family

Claude 3.5 Sonnet | GPT-4o | Gemini 1.5 Pro | DeepSeek Coder

Evaluation Metric

AST Code Correctness | Semantic Embedding Cosine | Exact Match | Rubric Scoring

Zero-Shot Reasoning Verified

Yes | No

Test Suite Verdict

Passed | Degraded | Failed Regression

Relations

• Audit Reference -> links to the formal review documentation or test repository.
• Target Architecture -> links to the deployed contract or autonomous agent runtime component.

Examples

Prompt

How do I configure and track AI Prompt Regression Test Matrix for our production environment?

Recommended Next Step

Generate the unified field schema, SQL DDL migration, and JSON validation schema to register into your system catalog.

Workflow: Define criteria -> Run automated verification -> Record baseline -> Monitor invariants.

Best Practices

• Enforce strict typing on numerical bounds and currency amounts; avoid unstructured free-text fields for critical states.
• Re-run validation test suites on every state-altering commit or parameter change.
• Keep example data synthetic and isolated from production cryptographic keys or private endpoints.

Limitations

• Provides architectural specifications, data models, and verification schemas; does not execute direct transaction signing without authorized external tooling.
• Requires network connectivity and valid RPC credentials when querying on-chain states.

Security & Safety Notes

• All parameters declare risk: safe. No unauthorized state modification or privileged credential access is performed.
• Use synthetic dummy keys and mock addresses in test suites and local verification scripts.

Common Pitfalls

• Problem: Mismatched decimal precision between contract runtime and database register. Solution: Always verify decimals using the explicit field mapping in this reference.
• Problem: Missing authorization checks prior to state update. Solution: Cross-validate against the Security Audit register before deployment.

Related Skills

• @ai-agent-tool-routing - covers tool schema registration and retry policy.
• @cross-chain-relayer-audit - covers message hashes, nonces and quorum proofs.
• @smart-contract-formal-verification - verifies state invariants mathematically.

Reusable Prompt

I want to establish a verified AI Prompt Regression Test Matrix register for our production protocol.
Guide me through the required field parameters and output the corresponding SQL DDL and JSON Schema.