Eval directory
Evals for Fireworks AI
6 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Fireworks AI AI products.
About Fireworks AI
Fireworks AI is a high-performance inference platform for open-source and fine-tuned models, delivering industry-leading throughput and latency for production workloads. Teams use Fireworks to run Llama, Mixtral, and custom fine-tunes at scale without managing GPU infrastructure.
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
6/6 packs
Scoring metrics
0/6 packs
Judge rubrics
6/6 packs
Use-case maps
0/6 packs
Pack context
6/6 packs
Available eval packs for Fireworks AI
6 packs ready to run.
Fireworks Batch Prompt Cache Runtime Performance
Evaluates Fireworks AI's Batch, Prompt Cache & Runtime Performance across 13 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
Fireworks Deployment Topology Capacity
Evaluates Fireworks AI's Deployment Topology & Capacity across 13 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
Fireworks Fine Tuning Multi Lora Serving
Evaluates Fireworks AI's Fine-Tuning & Multi-LoRA Serving across 12 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
Fireworks Function Calling Tool Orchestration
Evaluates Fireworks AI's Function Calling & Tool Orchestration across 12 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
Fireworks Safety Moderation Observability
Evaluates Fireworks AI's Safety, Moderation & Observability across 11 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
Fireworks Structured Outputs Grammar Constraints
Evaluates Fireworks AI's Structured Outputs & Grammar Constraints across 13 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
Why eval Fireworks AI AI
Fireworks AI'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 Fireworks AI 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 Fireworks AI's public product surface and runnable in Corsac with your own data.