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237 documents available
ā **Technical SEO Audit**
The Intelligent Research Assistant is a comprehensive AI-powered research platform built with a modular, scalable architecture. It combines document processing, vector search, multi-agent orchestration, fine-tuning capabilities, RLHF (Reinforcement Learning from Human Feedback), and enterprise-grade security into a unified system.
How do we evaluate our RAG system?
**Document Version:** 1.0
Comprehensive research on using Large Language Models (particularly DeepSeek, GPT-4, and Claude) for entity matching ground truth generation. This report covers LLM accuracy benchmarks, prompt engineering best practices, multi-LLM ensemble approaches, cost-benefit analysis, validation strategies, and patterns for converting LLM labels into regression tests.
This document describes how Agent Invest measures quality, detects regressions, and ensures safety. The system uses three evaluation layers: online scoring (every production run), offline evaluation (golden dataset), and guardrails (real-time safety checks).
Understanding how to **benchmark, evaluate, and compare LLMs** is essential for roles at Google, OpenAI, Anthropic, Cohere, and AI research teams. This file covers the most important benchmarks, evaluation methodologies, and how to build custom evaluation harnesses.
**AIP-C01 Study Guide ā Dr. Priya Ramanathan**
Evaluation is widely considered the **hardest unsolved problem** in LLM engineering. Unlike traditional software where a unit test returns pass/fail, LLM outputs are probabilistic, open-ended, and context-dependent -- there is no single "correct" answer for most tasks. Yet every production decision depends on evaluation: which model to deploy, whether a prompt change improved quality, whether a RAG pipeline is hallucinating less after a reranker upgrade. By mid-2025, benchmark saturation (fronti
root((Day 20: Evaluation & Benchmarks š))
url: "https://qdrant.tech/blog/qdrant-relari/"
title: Evaluating and Testing LLM Applications
**RAGAS** (Retrieval-Augmented Generation Assessment) is a specialized evaluation framework designed to measure RAG pipeline performance through reference-free metrics, making it ideal for production systems. **LangGraph** is a state-based orchestration framework that structures AI workflows as directed graphs. Integrating these two creates a powerful system for building and evaluating complex RAG pipelines systematically.
title: "Data-Driven RAG Evaluation: Testing Qdrant Apps with Relari AI"
The **EGG (Environmental, Governance & Goals) Rubric** is a comprehensive evaluation framework for assessing corporate sustainability performance across five critical sustainability themes. This rubric employs a multi-dimensional scoring approach that evaluates both the **quantity** and **quality** of corporate commitments, as well as their **specificity** and **temporal evolution**.
It doesn't matter how beautiful your theory is, <br>
Create a plan to build an n8n workflow that evaluates multiple LLM prompts for generating meal feedback using a **thinking model to generate ground truth** for comparison.
title: "Data-Driven RAG Evaluation: Testing Qdrant Apps with Relari AI"
Complete documentation for the `agent-eval` CLI, metrics, data formats, and customization.
title: Evaluation Framework
This guide explains how to evaluate the RAG (Retrieval-Augmented Generation) performance of the Clarity and Rigor agents using different retriever configurations.
This document defines the metrics used to evaluate the performance of different language models in generating Python game scripts. The metrics focus on three key areas: Accuracy, Bug Frequency, and Feature Completeness.
- **16-20 points**: Deep integration, NEAR standards usage, wallet integration, on-chain innovation
Run a text retrieval benchmark without generation (no LLM required).
