추천 학습 순서

1. 검색 기본: query, document, corpus, top-k, score, recall/precision
2. Embedding: vector, dimension, cosine similarity, semantic similarity
3. Dense retrieval: bi-encoder, embedding model, vector search
4. Sparse retrieval: TF-IDF, BM25, tokenizer
5. Vector index: ANN, HNSW, ef_search, payload filter
6. Qdrant: collection, point, vector, payload, upsert, filter
7. Hybrid retrieval: dense + sparse, score fusion, RRF
8. RAG: indexing pipeline, retrieval pipeline, context construction
9. Reranker/sLLM: candidate judge, structured output, fallback
10. Evaluation: Recall@K, MRR, NDCG, latency, failure cases