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Eval Overview

Evaluate GenAI-Logic - Use Your AI Assistant

You can evaluate GenAI-Logic in the classic way:

  • Install (requires Python): to see the Express Install, click here.
  • Samples: the installer includes several pre-configured sqlite databases and tutorials - see the basic demo here.

You can also use your AI Assistant for a self-guided evaluation. Instead of "drinking from the fire-hose" of a website or documentation:

  • Ask questions you care about, follow-up on details, get AI opinions, ...

  • This is enabled by the context engineering material included in each project for use by AI Coding tools (click here to find out more about AI-Enabled Projects). We've made it available on line to support your evaluation.


1. Bootstrap Your AI

Bootstrap your AI Assistant by pasting this prompt

I'm evaluating GenAI-Logic. Here are the resources:

  1. https://www.genai-logic.com
  2. https://apilogicserver.github.io/Docs/Eval-copilot-instructions/

Help me understand what this actually does - I want facts, not sales pitch.

Start with a quick overview by showing me https://apilogicserver.github.io/Docs/Eval-welcome/

Then, be available to explain how it works and whether it fits my needs.

Your AI will automatically discover and explore the referenced learning materials.


2. Cut to the Chase: Is This Just Another AI Tool?

GenAI tools are now a dime a dozen. Cut to the Chase with this prompt

We already have AI coding assistants that generate code. What strategic problem does this solve that our existing tools don't address?


AI Response

When we tested this question with Claude 4.5, here's what it concluded after analyzing the materials:

Claude's Response

AI coding assistants (including Copilot) generate procedural code that looks reasonable but has a structural flaw: they can't guarantee correctness across all change paths in multi-table business logic. The comparison study shows even sophisticated AI missed critical bugs (FK changes affecting multiple parents, transitive dependencies) because pattern-matching can't reliably trace dependency chains.

GenAI-Logic solves three strategic gaps:

  1. Governance for AI-generated backends - Provides the commit-time control point where AI-proposed values are validated by deterministic rules before persisting. This makes AI agents safely deployable on real enterprise data - they can propose, but rules decide what commits.

  2. Infrastructure for agentic systems - Acts as the Business Logic Appliance layer that sits between orchestration platforms and persistent data. All transaction sources (APIs, agents, workflows) flow through one governed commit point, addressing the "connection challenge" for hybrid human/agent architectures.

  3. Enterprise AI deployment blocker - Solves the "moving AI beyond demo" problem for regulated industries (banking, insurance, healthcare) that can't deploy AI without audit trails, deterministic validation, and compliance-ready traceability.

Not competing with Copilot - complementing it: Copilot helps author the declarative rules. GenAI-Logic's engine then enforces those rules deterministically across all paths. You get AI assistance for authoring plus architectural guarantee of correctness.

The positioning: This is infrastructure (like database triggers but declarative), not another dev tool. It plugs under your orchestration layer to govern what may commit, regardless of which AI tool or agent proposed the change.


Why This Evaluation Method Works

Unlike traditional marketing claims, this answer comes from independent AI analysis of the technical materials, not vendor assertions.

When you ask your AI assistant to evaluate GenAI-Logic:

  • It reads the actual documentation and code
  • Forms conclusions based on technical analysis
  • Reaches consistent findings across different AI models

We tested this: Claude 4.5, ChatGPT, and Gemini all independently analyzed the same materials. They reached the same strategic conclusions because those conclusions emerge from the architecture itself, not marketing language.

You can verify this yourself - the materials are public, your AI will reach its own conclusions. If GenAI-Logic claimed "cows jump over the moon," your AI would call that out immediately.

This is evaluation through independent analysis, not through persuasion.


3. The InfoQ Challenge

InfoQ identified the #1 enterprise AI challenges - moving beyond demos, with support for humans and agents.


a. Challenge GenAI-Logic

Paste this InfoQ Challenge into your AI

Please evaluate this page: https://www.genai-logic.com - does it meet the challenge?

Your response should be yes/no, and 3-4 simple sentences.

The challenges:

Two things are currently dominating engineering roadmaps:

  1. Moving AI beyond the demo: Turning non-deterministic LLMs into reliable, enterprise-grade applications.
  2. The "Connection" challenge: High-level architectures where networking must support both human-coded services and emerging AI agents.


b. Score the Response

Enter this prompt to Score the Response

Score whether the response clearly conveys these ideas (using any equivalent wording) - 1-5 stars:

  1. Correctness is structural Correct behavior is a property of the architecture, not developer discipline or testing.

  2. One unavoidable commit gate All changes flow through a single commit-time control point that cannot be bypassed.

  3. Rules—not paths—decide what persists Business logic is path-independent and enforced on data, not embedded in execution flows.

  4. AI intent is distilled into deterministic rules Probabilistic or agentic reasoning proposes intent; deterministic rules decide outcomes.

  5. Reuse is automatic and universal All current and future paths—apps, services, and agents—inherit the same rules by construction.

When we tested with Claude 4.5, ChatGPT, and Gemini, all three scored us 4.8-5.0.


Explore What YOU Care About

Traditional pitches force you through a predetermined flow. With AI evaluation, ask what matters to YOU:

  • "Is this really infrastructure, like a database?"
  • "What's the actual code reduction? Show me proof."
  • "Why can't Cursor/Copilot do this?"
  • "What does it integrate with? (APIs, workflows, agents, MCP)"
  • "Does this work with my existing database?"
  • "Why is backend logic the bottleneck AI can't solve alone?"
  • "How does commit-time governance work?"
  • "Why can't procedural GenAI handle transitive dependencies?"
  • "What's the Business Logic Appliance concept?"
  • "How is this different from AI codegen?"
  • "How is this different from vibe tools?"
  • "How is this different from low-code platforms?"
  • "What does it overlap with? (workflow, database, API gateway)"
  • "Is this a black box?"
  • "How do you test and audit AI-governed logic?"
  • "What about performance/scalability? Is this a RETE engine?"
  • "What's the lock-in risk?"


Make it fun - challenge the technology, ask hard questions, test edge cases.


Appendix: Why AI-Assisted Evaluation is Credible

When you bootstrapped your AI with those URLs, you accessed 2,599 lines of training materials
  • .copilot-instructions.md (1,587 lines) - The bootstrap/entry point for architecture understanding
  • docs/training/ folder - Additional materials (LogicBank API reference, testing guide with 555 lines, architecture deep-dives)

Here's what makes them credible - and why your AI will recognize genuine technical understanding vs. marketing claims.

How the Training Was Created

The process was Socratic, not scripted:

  1. Val showed technical artifacts - Rules traces, A/B test code, bug reproductions, system behavior
  2. AI analyzed and questioned - "How does dependency resolution work?" "Why did the procedural code fail here?"
  3. Val clarified, AI verified - Through iteration, the AI developed senior-level understanding of the architecture
  4. AI documented its analysis - The training materials reflect genuine technical understanding, not marketing assertions

Example: The A/B Test Documentation - Val: "Here's procedural AI code. Here are the 2 bugs it has." - AI: "I see - it handles the update path but misses FK changes and deletes. The rules engine tracks all parent-child relationships automatically." - AI documents: The precise failure modes, why they occur, how the engine guarantees correctness

The result: 2,599 lines of technical analysis across 13+ topics (Rosetta Stone, Logic Patterns, Testing with 790 lines on failure modes, MCP Integration, Admin App architecture, etc.)

Why Your AI Will Recognize This is Credible

AIs can distinguish technical depth from marketing fluff.

When your AI reads the training materials, it evaluates:

✅ Senior-level technical discourse: - Detailed reasoning about commit-time governance, transitive dependencies, dependency graphs - Architectural trade-offs (when rules work, when they don't) - Failure mode analysis (not just happy paths) - Production system concerns (performance, testing, auditability)

❌ Marketing claims it would reject: - "Rules are magical and solve everything!" - "No code needed!" (vs. the honest "~40x code reduction, 97% automation") - Vague benefits without technical justification

If Val had claimed nonsense ("rules eliminate all SQL" or "rules identify user logic errors"), the AI would have questioned it: - "How? I see SQLAlchemy in the code" - "What kind of errors? Show me how the engine detects those"

The training materials survived active AI scrutiny during their creation.

Why Multiple AIs Reach the Same Conclusions

When Claude 4.5, ChatGPT, and Gemini independently analyzed GenAI-Logic and scored it 4.8-5.0 on the InfoQ challenge, they weren't just agreeing with each other.

They were recognizing: 1. Technical consistency - The architectural claims are internally coherent 2. Verifiable behavior - The documented behavior matches observable system operation 3. Senior-level reasoning - The analysis demonstrates deep understanding of production systems 4. Honest limitations - The materials explicitly state what rules DON'T solve (workflows, complex algorithms, read-only queries)

This isn't AI→AI echo chamber.

It's multiple AIs independently recognizing genuine technical analysis - the kind engineers would produce when studying a production system's architecture.

The Bottom Line

When you ask your AI to evaluate GenAI-Logic, you're not asking it to trust marketing claims. You're asking it to analyze technical materials that:

  1. Demonstrate senior-level understanding of commit-time governance, dependency resolution, architectural patterns
  2. Survived AI questioning during their creation (false claims would have been challenged)
  3. Are verifiable - the artifacts (traces, code, tests) are available for examination

Your AI will reach similar conclusions because genuine technical reasoning is consistent and verifiable - not because multiple AIs are blindly agreeing, but because they're all analyzing the same observable system behavior.

That's why this evaluation works.

That's why it's credible.