Evaluation & Guardrails
Request SupportTesting, safety, and quality checks for LLM outputs and prompts so you ship with confidence and catch problems before users do.
Why it matters
LLMs can drift, hallucinate, or say the wrong thing. Evaluation measures whether outputs are accurate, on-topic, and safe; guardrails enforce rules (no PII, no off-brand tone, no harmful content) before responses reach users. Together they reduce risk and improve quality over time.
What we do
- Evaluation design – Define metrics (accuracy, relevance, safety, latency) and build test sets (golden Q&A, edge cases, adversarial prompts) so you can score model and prompt changes.
- Automated testing – Run evals in CI or on a schedule so regressions show up before release; we integrate with your repo or pipeline when possible.
- Guardrails – Add input and output checks: PII redaction, topic boundaries, blocklists, and format validation so bad or sensitive content is caught or filtered.
- Prompt and model iteration – Use eval results to improve prompts, RAG config, or model choice; we help you prioritize what to fix first.
Who it’s for
Teams that are already shipping LLM features and want to harden quality and safety without building everything in-house.
Next step
Tell us what you’re building (chatbot, API, internal tool), what could go wrong (hallucination, leakage, tone), and how you deploy. Request support and we’ll propose an evaluation and guardrail plan.