From bb8cac5a6ee0e6b28f7b91387b3e166252a4bf07 Mon Sep 17 00:00:00 2001 From: Dylan Couzon Date: Tue, 21 Apr 2026 12:00:10 -0400 Subject: [PATCH] update completion times --- .../headless/content/tutorials/search-engineering.md | 4 ++-- qdrant-landing/content/documentation/tutorials-lp-overview.md | 4 ++-- .../retrieval-quality-fundamentals.md | 2 +- .../retrieval-quality-golden-set.md | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/qdrant-landing/content/documentation/headless/content/tutorials/search-engineering.md b/qdrant-landing/content/documentation/headless/content/tutorials/search-engineering.md index 3bd2b0598..5041da568 100644 --- a/qdrant-landing/content/documentation/headless/content/tutorials/search-engineering.md +++ b/qdrant-landing/content/documentation/headless/content/tutorials/search-engineering.md @@ -5,8 +5,8 @@ | [Relevance Feedback](/documentation/tutorials-search-engineering/using-relevance-feedback/) | Relevance Feedback Retrieval in Qdrant | Python | 30m | Intermediate | | [Collaborative Filtering](/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | Python | 45m | Intermediate | | [Multivector Document Retrieval](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | Python | 30m | Intermediate | -| [Retrieval Quality Fundamentals](/documentation/tutorials-search-engineering/retrieval-quality-fundamentals/) | Understand evaluation levels and choose the right metric. | Python | 15m | Intermediate | -| [Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/) | Generate ground truth data at scale and avoid data leakage. | Python | 20m | Intermediate | +| [Retrieval Quality Fundamentals](/documentation/tutorials-search-engineering/retrieval-quality-fundamentals/) | Understand evaluation levels and choose the right metric. | Python | 20m | Intermediate | +| [Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/) | Generate ground truth data at scale and avoid data leakage. | Python | 40m | Intermediate | | [Retrieval Quality Evaluation](/documentation/tutorials-search-engineering/retrieval-quality/) | Measure ANN recall and tune HNSW parameters. | Python | 30m | Intermediate | | [Hybrid Search with Reranking](/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | Python | 40m | Intermediate | | [Semantic Search for Code](/documentation/tutorials-search-engineering/code-search/) | Navigate codebases using vector similarity. | Python | 45m | Intermediate | diff --git a/qdrant-landing/content/documentation/tutorials-lp-overview.md b/qdrant-landing/content/documentation/tutorials-lp-overview.md index 08cafbb47..aae6e5e3f 100644 --- a/qdrant-landing/content/documentation/tutorials-lp-overview.md +++ b/qdrant-landing/content/documentation/tutorials-lp-overview.md @@ -68,8 +68,8 @@ partition: qdrant | [Semantic Search Basics](/documentation/tutorials-search-engineering/neural-search/) | Deploy a search service for company descriptions. | FastAPI | 30m | Beginner | | [Collaborative Filtering](/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | Python | 45m | Intermediate | | [Multivector Document Retrieval](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | Python | 30m | Intermediate | -| [Retrieval Quality Fundamentals](/documentation/tutorials-search-engineering/retrieval-quality-fundamentals/) | Understand evaluation levels and choose the right metric. | Python | 15m | Intermediate | -| [Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/) | Generate ground truth data at scale and avoid data leakage. | Python | 20m | Intermediate | +| [Retrieval Quality Fundamentals](/documentation/tutorials-search-engineering/retrieval-quality-fundamentals/) | Understand evaluation levels and choose the right metric. | Python | 20m | Intermediate | +| [Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/) | Generate ground truth data at scale and avoid data leakage. | Python | 40m | Intermediate | | [Retrieval Quality Evaluation](/documentation/tutorials-search-engineering/retrieval-quality/) | Measure ANN recall and tune HNSW parameters. | Python | 30m | Intermediate | | [Reranking for Better Search](/documentation/search-precision/reranking-semantic-search/) | Use multivector representations for better ranking. | Python | 30m | Intermediate | | [Hybrid Search with Reranking](/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | Python | 40m | Intermediate | diff --git a/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-fundamentals.md b/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-fundamentals.md index 9ed1b0fd8..30848008b 100644 --- a/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-fundamentals.md +++ b/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-fundamentals.md @@ -7,7 +7,7 @@ aliases: # Retrieval Quality Fundamentals -| Time: 15 min | Level: Intermediate | | | +| Time: 20 min | Level: Intermediate | | | |--------------|---------------------|--|----| Before measuring retrieval quality, it's worth understanding what you're measuring. Retrieval quality operates at three distinct levels, and it's easy to optimize for the wrong one. diff --git a/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-golden-set.md b/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-golden-set.md index e85adbf55..5c7df6827 100644 --- a/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-golden-set.md +++ b/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-golden-set.md @@ -7,7 +7,7 @@ aliases: # Building a Golden Query Set -| Time: 20 min | Level: Intermediate | | | +| Time: 40 min | Level: Intermediate | | | |--------------|---------------------|--|----| Evaluating retrieval relevance requires a labeled dataset of queries paired with their expected relevant documents (commonly called a *golden query set* or *ground truth*). The [Retrieval Quality Evaluation](/documentation/tutorials-search-engineering/retrieval-quality/) tutorial measures **ANN recall** against exact kNN, which needs no relevance labels. This page covers the separate task of building labeled data to measure **retrieval relevance** against real user intent.