RLHF and Evaluation Data for More Reliable LLMs and AI Agents

LLM data labeling produces the judgments a model learns from: preference rankings, rubric scores, grounded citations and agent traces. Generic annotation tags objects or text spans. LLM labeling scores whole responses against rubrics, so reviewer calibration and agreement tracking matter more than box accuracy.

Gold tasks, consensus, blind checks and disagreement scoring, with review gates before export. Scorer drift is monitored, guidelines are versioned and every label traces back to the guideline and reviewer behind it.

Access runs through SSO and SAML, SCIM, least-privilege RBAC and IP allowlists. Data is encrypted with customer-managed keys, projects can run in your cloud, and immutable audit logs record every action. A pre-processor can redact PII at ingestion.

Connect S3, Azure or GCS, use the REST API and SDKs, receive webhooks and export JSONL or CSV. Pilots start in days, with the exact timeline set at scoping.

Score each answer claim by claim: link it to a source span, flag unsupported spans and classify the error. Roll the labels up into grounding and hallucination rates you can track across model and retrieval changes.

RLHF data is human judgment on model outputs, used to train a reward model and align an LLM. A dataset holds prompts, candidate responses, point-wise scores or pairwise rankings against a rubric, reviewer agreement and the guideline version behind each label, exported as clean splits for SFT and RLHF.

AI red teaming data is adversarial prompts and scored model responses that show where a model fails on jailbreaks, PII leaks, toxicity and bias. Taskmonk generates the adversarial prompts, scores the responses with calibrated reviewers, triages by severity and tracks mitigation across releases. It supplies training and evaluation data, and does not replace a security penetration test.

LLM evaluation scores a single response, while agent evaluation scores the whole task, including tool calls, planner-executor steps and recovery attempts. Taskmonk labels both: response-level rubrics for chat, Q&A and summarization, and trace-level labels for agents.

Agent trace annotation labels each step of an agent run: the function called, its arguments, the tool output and the outcome. Those labels teach agents to pick the right tool, format arguments correctly and recover from failure.

Calibrated evaluators who are screened, assessed and certified before they enter production, then trained on your rubric with exemplars. Agreement and scorer drift are monitored, and the pool spans 1,763+ qualified experts across 20+ languages.