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© 2026 nexoqoai.tech. Nexoqo is the AI QualityOps platform.

Make production AI measurable, testable, and dependable.

RAG Evaluation

Evaluate retrieval and generation together.

Measure Retrieval-Augmented Generation systems across both the quality of retrieved context and the quality of the generated answer.

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Part of the Nexoqo AI QualityOps platform

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Overview

Evaluate retrieval and generation together.

Measure Retrieval-Augmented Generation systems across both the quality of retrieved context and the quality of the generated answer.

What this capability evaluates

Apply repeatable quality criteria to the behaviors and signals that matter for this part of the AI workflow.

Quality outcome

Trace answer-quality problems back to retrieval, context, or generation behavior.

Evaluation areas

Measure the behavior behind the result.

Use criteria that reflect the complete AI workflow and the quality requirements of the product.

01

Context relevance

Include this signal in a repeatable evaluation and compare it across AI-system changes.

02

Retrieval precision

Include this signal in a repeatable evaluation and compare it across AI-system changes.

03

Retrieval recall

Include this signal in a repeatable evaluation and compare it across AI-system changes.

04

Answer relevance

Include this signal in a repeatable evaluation and compare it across AI-system changes.

05

Groundedness

Include this signal in a repeatable evaluation and compare it across AI-system changes.

06

Citation correctness

Include this signal in a repeatable evaluation and compare it across AI-system changes.

07

Faithfulness

Include this signal in a repeatable evaluation and compare it across AI-system changes.

08

Context utilization

Include this signal in a repeatable evaluation and compare it across AI-system changes.

Quality outcome

Turn evaluation into release confidence.

Trace answer-quality problems back to retrieval, context, or generation behavior.

Part of a continuous workflow

Connect test results with regression analysis, production monitoring, and defined quality thresholds as the AI system evolves.

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Related quality capabilities.

Connect this workflow with the other layers of continuous AI evaluation.

AI Agent Testing

Test autonomous and semi-autonomous agents against realistic task scenarios, evaluating both the final result and the sequence of actions used to reach it.

LLM Evaluation

Run repeatable evaluations on LLM outputs using deterministic checks, semantic methods, model-based judges, custom evaluators, and human review.

Regression Testing

Run an established evaluation suite against prompt, model, retrieval, workflow, or agent updates and compare the results with a previous quality baseline.

RAG Evaluation

Make AI quality measurable.

Discuss how continuous evaluation can fit the AI systems and quality criteria your team is building.

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