LLM Output Evaluation
Assess factual accuracy, groundedness, instruction following, hallucination, citation quality and safety.
LLM & Agent Evaluation Systems
Make AI Quality Measurable in Guangzhou
Build repeatable evaluation systems for LLMs, RAG applications and AI agents so failures become structured data your engineering team can diagnose and improve.
Trusted by ambitious AI teams
DittoCLIKA.ZYCUS
Evaluation Beyond a Single Score
AI systems can appear fluent while failing on factual accuracy, groundedness, retrieval quality, citations, tool calls or task completion. Evaluation needs to reflect the behavior your application actually requires.
XIVTech designs evaluation datasets, criteria and review loops that make quality measurable across model outputs and complete agent trajectories.
Evaluation Capabilities
RAG Evaluation
Measure retrieval quality, context relevance, answer grounding and failure patterns across representative questions.
Agent Evaluation
Evaluate planning, tool selection, tool-call accuracy, escalation, task completion and error recovery.
Failure Taxonomies
Turn evaluation results into categories and examples that guide engineering and product decisions.
Automated and Human Review
Combine automated checks, rubric-based scoring and expert review where each is most useful.
Continuous Evaluation
Run repeatable checks as prompts, retrieval, tools, models and application behavior evolve.
What We Deliver
Criteria and Rubrics
Define application-specific quality criteria, scoring rules and examples for expected and unacceptable behavior.
Evaluation Datasets
Create representative test cases covering normal usage, edge cases, adversarial inputs and known failures.
Evaluation Harnesses
Connect model, retrieval and agent runs to repeatable automated checks, reports and review workflows.
Regression Testing
Compare changes over time so improvements in one area do not silently introduce failures in another.
From Behavior to Improvement
Evaluation work follows the same prepare, measure, diagnose and improve loop used across AI data engineering.
- 01
Map the System
Document models, prompts, retrieval, tools, escalation paths and the tasks users need completed.
- 02
Define Quality
Create criteria, rubrics, datasets and thresholds aligned to application behavior.
- 03
Run Evaluations
Combine automated evaluation with human review for ambiguous, high-impact or safety-sensitive cases.
- 04
Diagnose Failures
Classify failures and identify whether the cause is data, retrieval, prompting, tooling or model behavior.
- 05
Regress and Improve
Turn findings into updated data, implementation changes and a continuously maintained evaluation suite.