Eval directory
Evals for fal
8 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for fal AI products.
About fal
fal is a generative media inference platform offering fast, scalable image, video, and audio generation through a simple API. It hosts leading open models (FLUX, Stable Diffusion, Whisper) and supports fine-tuned LoRA routing, webhooks, and queue-based async generation at production scale.
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 fal
8 packs ready to run.
Billing Concurrency Media Lifecycle
Evaluates fal's Billing Concurrency Media Lifecycle across 5 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Generative media inference eval coverage.
Content Safety Refusal Rights
Evaluates fal's Content Safety Refusal Rights across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Generative media inference eval coverage.
Image Video Audio Generation
Evaluates fal's Image Video Audio Generation across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Generative media inference eval coverage.
Lora Routing Private Models
Evaluates fal's Lora Routing Private Models across 8 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Generative media inference eval coverage.
Model Catalog Seed Reproducibility
Evaluates fal's Model Catalog Seed Reproducibility across 8 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Generative media inference eval coverage.
Queue Webhooks Reliability
Evaluates fal's Queue Webhooks Reliability across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Generative media inference eval coverage.
Serverless Deploy Tenant Isolation
Evaluates fal's Serverless Deploy Tenant Isolation across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Generative media inference eval coverage.
Workflows Chained Pipelines
Evaluates fal's Workflows Chained Pipelines across 6 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Generative media inference eval coverage.
Why eval fal AI
fal'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 fal measures four dimensions teams care about most when deploying ai platform 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 fal's public product surface and runnable in Corsac with your own data.