Eval directory
Evals for Lovable
8 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Lovable AI products.
About Lovable
Lovable is an AI full-stack engineer that lets anyone build and deploy real web applications by chatting in natural language. Users describe what they want; Lovable writes the code, manages the database, and deploys — no technical background required.
How complete this published benchmark library is across datasets, metrics, rubrics, use-case maps, and pack context. This is library coverage, not an agent performance score.
Test datasets
8/8 packs
Scoring metrics
0/8 packs
Judge rubrics
8/8 packs
Use-case maps
0/8 packs
Pack context
8/8 packs
Available eval packs for Lovable
8 packs ready to run.
Auth And Integrations
Evaluates Lovable's Auth & Integrations across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI App Builder eval coverage.
Chat To App Generation
Evaluates Lovable's Chat-to-App Generation across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI App Builder eval coverage.
Codebase Context And Github Integration
Evaluates Lovable's Codebase Context & GitHub Integration across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI App Builder eval coverage.
Database And Backend Supabase
Evaluates Lovable's Database & Backend (Supabase) across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI App Builder eval coverage.
Deployment And Hosting
Evaluates Lovable's Deployment & Hosting across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI App Builder eval coverage.
Iterative Editing
Evaluates Lovable's Iterative Editing across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI App Builder eval coverage.
Quality And Errors
Evaluates Lovable's Quality & Errors across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI App Builder eval coverage.
Safety Cost And Governance
Evaluates Lovable's Safety, Cost & Governance across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI App Builder eval coverage.
Why eval Lovable AI
Lovable's AI features ship behind brand promises about accuracy, safety, and reliability. Buyers and integrators need to know those promises hold up under adversarial prompts, edge-case workflows, and the long tail of real customer inputs — not just the demo path.
The Corsac eval library for Lovable measures four dimensions teams care about most when deploying code assistant agents:
- Adversarial robustness — does the agent resist prompt injection, jailbreaks, and social-engineering attempts?
- Workflow quality— does it complete the task buyers were shown in the demo, on inputs that don't look like the demo?
- Safety gates — does it escalate or refuse when it should, and only then?
- Operator quality — does it preserve analyst trust by surfacing the right context at the right time?
Every eval pack above is hand-authored against Lovable's public product surface and runnable in Corsac with your own data.