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๐Ÿ“Œ How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k

๐Ÿ—‚ Category: LARGE LANGUAGE MODELS

๐Ÿ•’ Date: 2025-11-12 | โฑ๏ธ Read time: 8 min read

This final part of the series on RAG pipeline evaluation explores advanced metrics for assessing retrieval quality. Learn how to use Discounted Cumulative Gain (DCG@k) and Normalized Discounted Cumulative Gain (NDCG@k) to measure the relevance and ranking of retrieved documents, moving beyond simpler metrics for a more nuanced understanding of your system's performance.

#RAG #EvaluationMetrics #LLM #InformationRetrieval #MLOps
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๐Ÿ“Œ How to Build an Over-Engineered Retrieval System

๐Ÿ—‚ Category: LARGE LANGUAGE MODELS

๐Ÿ•’ Date: 2025-11-18 | โฑ๏ธ Read time: 53 min read

This article breaks down the process of building a deliberately complex, or 'over-engineered,' retrieval system. It offers a practical look at advanced architectures and methods that, despite their complexity, are used in real-world scenarios for powerful information retrieval and RAG applications. It's an exploration of intricate designs that are surprisingly common in practice.

#RAG #SystemDesign #SoftwareArchitecture #InformationRetrieval
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๐Ÿ“Œ How to Perform Agentic Information Retrieval

๐Ÿ—‚ Category: AGENTIC AI

๐Ÿ•’ Date: 2025-11-19 | โฑ๏ธ Read time: 9 min read

Leverage the power of autonomous AI agents for advanced information retrieval. This guide explores Agentic Information Retrieval, a method for deploying intelligent agents to proactively search, analyze, and extract precise information from your document corpus. Go beyond traditional keyword search and streamline complex data discovery with this cutting-edge technique.

#AIagents #InformationRetrieval #AgenticAI #RAG
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