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3,528 documents available
[Task Definitions] → [Model Interface] → [Inference Execution] → [Scoring] → [Reports]
This document tracks planned features and completed work. Future roadmap items are listed first, followed by completed features.
Design an **end-to-end evaluation pipeline** for a production **LLM-based product** (assistant, RAG app, code copilot, or agent). The pipeline must answer: **“Did this model / prompt / retrieval change make the product better, safer, or cheaper — and can we prove it?”** It spans **offline** lab benchmarks, **task-specific metrics**, **LLM-as-judge**, **human preference** studies, **safety** testing, **golden-set regression**, and **online** A/B experimentation — with **dashboards and alerting**
title: "Air-Gapped RAG: Grounding, Citations, and Evaluation"
Generative models produce **open-ended text** — there is rarely a single “correct” string. Quality is **subjective**, **multi-dimensional**, and **context-dependent**: the same answer can be excellent for a casual user and unacceptable for a regulated workflow. Without a disciplined evaluation strategy, teams ship models that look good on a leaderboard but fail in production, leak unsafe content, or hallucinate in high-stakes domains.
**Participants**: Ryotaro, Masaki Adachi
title: "LLM Evaluation Cheat Sheet"
This document provides a practical reference for using the Knowledge MCP to research evaluation placement, methods, and anti-patterns. It shows which queries to run and what to expect from each.
**Module 07: Evaluation and Testing**
> 70+ active-recall questions. Pair with `LLM_EVALUATION_DEEP_DIVE.md`.
**Target folder:** `testing/judges/`, `testing/fixtures/`, CI via `just check`
In the Second Renaissance, the greatest failure of the amateur is the **fetishization of the first completion.** We reject the culture of ninety-percent building and ten-percent evaluation. This ratio is a recipe for **institutional model collapse.** Building a system that produces a plausible-looking output is a trivial act. Building a system whose failure modes are bounded, quantified, and recoverable is the concretion of **engineering sovereignty.**
> If it's not measured, it's not accurate. Ship blind and children pay the price.
1. [Importance and Challenges of Evaluation](#why-is-evaluation-so-critical-when-developing-search-and-rag-systems-with-embeddings-and-rerankers-and-what-are-the-main-challenges-involved)
This document details the exact execution flow of the system and the offline validation and evaluation framework implemented using RAGAs.
Define a rigorous, evidence-based evaluation framework for the ProtoExtract
*In this first article of a three-part (monthly) series, we introduce RAG evaluation, outline its challenges, propose an effective evaluation framework, and provide a rough overview of the various tools and approaches you can use to evaluate your RAG application.*
**Author:** Mike Chavez
> Testing LLMs is not like unit testing software. There's no single correct output, no simple assertion to make, and behavior drifts without any code change. Here's how to build a testing culture that actually catches real problems.
This document outlines the complete evaluation strategy for Aegis AI Video Censoring Platform MVP, covering core metrics, test suites, user testing protocols, regression testing, and a measurement timeline from Weeks 4-15.
This guide provides a systematic approach for auditing User Experience (UX) in commercial SaaS applications, rooted in heuristic evaluation and modern design systems.
- ✅ TensorFlow MNIST CNN モデル訓練
This guide explains different ways to preview the UI components before deploying your application.
title: 'LLM as a Judge Evaluation Guide'
