<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Evaluation on AI Charcha</title><link>https://www.aicharcha.com/tags/evaluation/</link><description>Recent content in Evaluation on AI Charcha</description><image><title>AI Charcha</title><url>https://www.aicharcha.com/images/aicharcha-logo-refresh-1.svg</url><link>https://www.aicharcha.com/images/aicharcha-logo-refresh-1.svg</link></image><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 19 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.aicharcha.com/tags/evaluation/index.xml" rel="self" type="application/rss+xml"/><item><title>AI Agent Governance Metrics for 2026</title><link>https://www.aicharcha.com/research/ai-agent-governance-metrics-2026/</link><pubDate>Fri, 19 Jun 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/research/ai-agent-governance-metrics-2026/</guid><description>A research note on the governance metrics teams should track when AI agents move from experiments into workflow automation.</description></item><item><title>AI Workflow Auditability Framework for 2026</title><link>https://www.aicharcha.com/research/ai-workflow-auditability-framework-2026/</link><pubDate>Thu, 18 Jun 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/research/ai-workflow-auditability-framework-2026/</guid><description>A research framework for making AI-assisted workflows easier to audit, review, explain, and improve across teams.</description></item><item><title>How to Build Generative AI Apps in Azure with Microsoft Foundry</title><link>https://www.aicharcha.com/guides/how-to-build-generative-ai-apps-in-azure-microsoft-foundry/</link><pubDate>Thu, 18 Jun 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/guides/how-to-build-generative-ai-apps-in-azure-microsoft-foundry/</guid><description>A practical guide to building generative AI apps in Azure with Microsoft Foundry, covering project setup, model selection, SDK development, RAG, fine-tuning, responsible AI, and evaluation.</description></item><item><title>Context Engineering Evaluation Framework for AI Teams</title><link>https://www.aicharcha.com/research/context-engineering-evaluation-framework-2026/</link><pubDate>Wed, 17 Jun 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/research/context-engineering-evaluation-framework-2026/</guid><description>A research note on evaluating context engineering quality across prompts, retrieval, memory, source selection, and workflow outcomes.</description></item><item><title>AI Search Reliability in 2026: What Teams Need to Know Before They Trust It</title><link>https://www.aicharcha.com/research/ai-search-reliability-2026/</link><pubDate>Thu, 11 Jun 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/research/ai-search-reliability-2026/</guid><description>An in-depth analysis of AI search accuracy, hallucination risk, citation quality, and what reliability actually means for research and business workflows.</description></item><item><title>How to Choose the Right AI Tool</title><link>https://www.aicharcha.com/guides/how-to-choose-the-right-ai-tool/</link><pubDate>Thu, 11 Jun 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/guides/how-to-choose-the-right-ai-tool/</guid><description>A practical framework for choosing the best AI tool based on use case, budget, team size, privacy, integrations, workflow fit, and adoption risk.</description></item><item><title>Enterprise RAG Evaluation Methods for 2026</title><link>https://www.aicharcha.com/research/enterprise-rag-evaluation-methods-2026/</link><pubDate>Fri, 05 Jun 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/research/enterprise-rag-evaluation-methods-2026/</guid><description>A research note on evaluating retrieval-augmented generation systems for accuracy, source quality, coverage, and user trust.</description></item><item><title>Synthetic Data for AI Testing in 2026</title><link>https://www.aicharcha.com/research/synthetic-data-for-ai-testing-2026/</link><pubDate>Wed, 03 Jun 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/research/synthetic-data-for-ai-testing-2026/</guid><description>A research note on using synthetic data to test AI workflows, protect sensitive information, and improve evaluation coverage.</description></item><item><title>AI Trust Metrics for Leaders and Teams</title><link>https://www.aicharcha.com/research/may-31-ai-trust-metrics/</link><pubDate>Sun, 31 May 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/research/may-31-ai-trust-metrics/</guid><description>A research note on measuring trust in AI systems through reliability, transparency, control, user confidence, and business outcomes.</description></item><item><title>AI Output Quality Assurance for Business Workflows</title><link>https://www.aicharcha.com/research/may-29-ai-output-quality-assurance/</link><pubDate>Fri, 29 May 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/research/may-29-ai-output-quality-assurance/</guid><description>A practical research note on ai output quality assurance for business workflows, with decision criteria, rollout patterns, risks, metrics, and next steps for teams evaluating AI in 2026.</description></item><item><title>Evaluation Scorecards for LLM Applications</title><link>https://www.aicharcha.com/research/may-22-evaluation-scorecards-for-llm-apps/</link><pubDate>Fri, 22 May 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/research/may-22-evaluation-scorecards-for-llm-apps/</guid><description>A research note on building scorecards for LLM apps using accuracy, usefulness, safety, latency, cost, and review effort.</description></item><item><title>AI Product Analytics Metrics That Actually Matter</title><link>https://www.aicharcha.com/research/may-18-ai-product-analytics-metrics/</link><pubDate>Mon, 18 May 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/research/may-18-ai-product-analytics-metrics/</guid><description>A research note on measuring AI product usage, quality, latency, cost, review load, retention, and task success.</description></item><item><title>Synthetic Test Sets for AI Tool Evaluation</title><link>https://www.aicharcha.com/research/may-09-synthetic-test-sets-for-ai-tools/</link><pubDate>Sat, 09 May 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/research/may-09-synthetic-test-sets-for-ai-tools/</guid><description>A research note on using synthetic test sets to compare AI tools, check regressions, and evaluate quality before rollout.</description></item><item><title>RAG Source Quality Scoring for Reliable AI Answers</title><link>https://www.aicharcha.com/research/may-04-rag-source-quality-scoring/</link><pubDate>Mon, 04 May 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/research/may-04-rag-source-quality-scoring/</guid><description>A research note on how source quality scoring can improve retrieval augmented generation and reduce weak or unsupported AI answers.</description></item><item><title>AI Model Routing Architectures for Cost and Quality</title><link>https://www.aicharcha.com/research/may-03-ai-model-routing-architectures/</link><pubDate>Sun, 03 May 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/research/may-03-ai-model-routing-architectures/</guid><description>A research note on model routing patterns that send tasks to different AI models based on cost, risk, latency, and quality needs.</description></item><item><title>AI Workflow Evaluation Framework for Practical Teams</title><link>https://www.aicharcha.com/research/may-01-ai-workflow-evaluation-framework/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://www.aicharcha.com/research/may-01-ai-workflow-evaluation-framework/</guid><description>A practical research note on ai workflow evaluation framework for practical teams, with decision criteria, rollout patterns, risks, metrics, and next steps for teams evaluating AI in 2026.</description></item></channel></rss>