'ndcg' 검색 결과

Design and improve retrieval for RAG, which supplies external knowledge to AI answers. Cover document splitting, semantic and keyword search, combined result ranking, and quality measurements to identify where relevant information is being missed.

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• Building search for a knowledge base
• Comparing semantic, keyword, and hybrid search
• Diagnosing missed search results

Design a RAG pipeline that retrieves documents to support AI answers. Analyze document splitting, select embedding models and vector databases, and evaluate retrieval quality using test queries and known relevant documents.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a RAG system for internal documents
• Choosing how to split documents for search
• Comparing embedding models and vector databases

Use when a user asks an agent to evaluate or tune text search similarity for an existing Viking AI Search application and dataset.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Semble (MinishLab) MCP wrapper — semantic code search via embeddings (98% token reduction vs grep+read, NDCG@10=0.854)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Evaluate recommender system quality for the CSX4207 Vinyl Record Store. Use whenever you must compute or report recommender metrics (Precision@k, Recall@k, HitRate@k, MRR, MAP@k, NDCG@k, coverage, diversity, novelty, serendipity, personalization), design an evaluation protocol/split, run a baseline comparison, or write the evaluation section of a course deliverable. Covers formulas, JavaScript reference implementations, and the reporting checklist.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.