Eval directory
Evals for Perplexity
8 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Perplexity AI products.
About Perplexity
Perplexity is an answer engine; the Perplexity Sonar API exposes its grounded LLM with real-time web search and inline citations — sonar, sonar-pro, and sonar-reasoning models, source filtering and recency controls, and OpenAI-compatible chat completions for grounded answers at API 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 Perplexity
8 packs ready to run.
Auth Rate Limits Tiers And Cost
Evaluates Perplexity's Auth, Rate Limits, Tiers & Cost across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Grounded Answer API eval coverage.
Chat Completions Openai Compatible
Evaluates Perplexity's Chat Completions (OpenAI-compatible) across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Grounded Answer API eval coverage.
Citations And Source Grounding
Answer RelevanceEvaluates Perplexity's Citations & Source Grounding across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Grounded Answer API eval coverage.
Images And Multimodal
Evaluates Perplexity's Images & Multimodal across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Grounded Answer API eval coverage.
Reasoning Models Sonar Reasoning
Evaluates Perplexity's Reasoning Models (Sonar Reasoning) across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Grounded Answer API eval coverage.
Safety Policy Source Quality And Governance
Evaluates Perplexity's Safety, Policy, Source Quality & Governance across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Grounded Answer API eval coverage.
Search Controls
Evaluates Perplexity's Search Controls across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Grounded Answer API eval coverage.
Structured Outputs And Format Controls
Evaluates Perplexity's Structured Outputs & Format Controls across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Grounded Answer API eval coverage.
Why eval Perplexity AI
Perplexity'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 Perplexity 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 Perplexity's public product surface and runnable in Corsac with your own data.