Blueprint Overview
This specification defines the complete evaluation architecture, stratified golden datasets, and multi-tiered grading rubrics required to measure non-deterministic AI and agentic workflow quality in production. It aligns product management, applied AI engineering, and domain experts on programmatic assertions, calibrated LLM-as-a-Judge scoring criteria, adversarial guardrail checks, and quantitative release gates. Teams use it to eliminate subjective quality reviews, prevent customer-facing hallucinations, and enforce strict latency and token-cost ceilings across model and prompt iterations.
Example Output for this Framework
Excerpt based on a fictitious company example
LLM Evaluation Rubric & Dataset Spec: ClauseGuard Agent (v2.4)
- Document ID: SPEC-EVAL-2026-094
- Product Capability: ClauseGuard Agent (Autonomous Third-Party MSA & DPA Redline Counter-Proposal Workflow)
- Organization: KinetixAI (Enterprise Procurement & Autonomous Contract Negotiation Platform)
- Author: Principal AI Product Manager & Applied AI Lead
- Target Buyer & End-User Personas: General Counsel (GC), VP of Legal Operations, Senior Commercial Counsel
- Architecture Pattern: Multi-Step Agentic RAG (Document Parser -> Playbook Vector + Graph Retriever -> Clause Risk Classifier -> Counter-Redline Generator -> Citation Verifier)
- Release Target: Q4 Enterprise GA (Tier-1 Regulated Accounts)
1. Evaluation Objectives & Business Quality Dimensions
Enterprise General Counsel and VPs of Legal Operations adopt KinetixAI to compress third-party contract review cycles from 11 business days to under 20 minutes without expanding outside counsel spend. However, autonomous contract redlining carries zero tolerance for fabricated statutory citations, unflagged uncapped liability clauses, or unauthorized concessions that violate the customer's corporate legal playbook. A single hallucinated indemnity carve-out or missed GDPR cross-border transfer obligation during a live negotiation immediately disqualifies ClauseGuard Agent in enterprise security and legal audits.
This specification establishes the formal offline and online evaluation architecture required to promote ClauseGuard Agent from...
⚡ How It Works
Define Targeting
Select any product, persona, or industry targeting as desired. Marcora automatically pulls in all your relevant business context.
Specify Focus
Describe the AI or agentic capability being evaluated, including the expected input payloads, retrieval sources, tool invocations, and structured output schemas. Specify the most critical business failure modes, compliance risks, or hallucination patterns that must be prevented for your target buyer and end-user personas. Provide your target golden dataset composition, domain expert calibration expectations, and quantitative release thresholds for accuracy, p95 latency, and unit token cost.
Create & Refine
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Your generated document serves as a strong foundation you can refine independently or with Marcora's AI assistant.
Blueprint DNA Technology
This blueprint includes advanced structural analysis for intelligent content generation
Smart Structure
Understands section hierarchy and content relationships
Tone Analysis
Maintains consistent voice and style across all outputs
Format Intelligence
Adapts length and formatting for different contexts
Context Adaptation
Intelligently customizes content for specific audiences
Contributor's Story
Shipping an AI or agentic capability based on ad-hoc "vibe checks" is a recipe for production hallucinations and eroded enterprise trust. Writing a formal evaluation specification before alpha launch lets our product managers turn subjective domain expertise into a stratified golden dataset, programmatic assertions, and calibrated LLM-as-a-Judge rubrics so engineering can iterate on prompts, tools, and models against crisp release gates with confidence.
