Evaluation articles

Schedule agent evaluations and backfill missed days

Run recurring agent evaluations and controlled historical backfills with Polyaxon schedules, date-range matrices, explicit window identity, and fresh results.

Build an LLM pipeline to classify ML failure reports

Use Polyaxon DAGs, tracked outputs, artifacts, and run comparison to develop a reviewable classifier for ML workload failure reports.

Build and evaluate LLM function calling

Connect bounded LLM tools to Polyaxon sandboxes, validate calls in the host, retain execution receipts, and compare tool behavior in repeatable evaluation runs.

Build and evaluate AI agent guardrails in Polyaxon

Use Polyaxon to version guardrail experiments, compare case-level outcomes, constrain execution, and require evidence before promoting an agent release.

Cache pipeline steps without hiding LLM regressions

Cache deterministic Polyaxon preparation steps while forcing fresh LLM evaluations, using explicit dependency identities and separate cache policies.

Evaluate zero-shot prompting for production tasks

Use Polyaxon runs, versioned components, grid searches, case artifacts, and comparison views to qualify zero-shot prompts for production ML workflows.

Stop expensive agent evaluation sweeps early

Use Polyaxon failure and metric early-stopping rules to bound agent evaluation sweeps while preserving complete evidence and avoiding premature quality decisions.

Tune RAG retrieval with Polyaxon experiment matrices

Compare RAG chunk sizes, retrieval depth, and model choices with Polyaxon experiment matrices, fixed evaluation inputs, and quality-aware run comparisons.

Optimize LLM performance and cost with controlled experiments

Track usage and pricing assumptions in Polyaxon, compare cost against quality in run dashboards, and control resources and concurrency during LLM experiments.