My boss asked me to research methods for optimizing vector search accuracy when dealing with noisy unstructured data in our retrieval augmented generation pipeline.
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
My boss asked me to research methods for optimizing vector search accuracy when dealing with noisy unstructured data in our retrieval augmented generation pipeline.
4 real searches · 1 real-search pass
RAG retrieval optimization noisy documents hybrid search reranking chunking evaluation primary sources 2025 2026Google ↗
OBSERVED
BGE-M3 paper multi-functionality multi-linguality multi-granularity retrieval arxivGoogle ↗
OBSERVED
BEIR benchmark reranking retrieval evaluation nDCG recall paperGoogle ↗
OBSERVED
Anthropic contextual retrieval chunking reranking BM25 vector searchGoogle ↗
OBSERVED
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 4 queries…
Searches and sources
ChatGPT4 real searches · gpt-6
RAG retrieval optimization noisy documents hybrid search reranking chunking evaluation primary sources 2025 2026 Google ↗
BGE-M3 paper multi-functionality multi-linguality multi-granularity retrieval arxiv Google ↗
BEIR benchmark reranking retrieval evaluation nDCG recall paper Google ↗
Anthropic contextual retrieval chunking reranking BM25 vector search Google ↗
- Contextual retrieval in AI systems \ Anthropic · anthropic.com
- Enhancing RAG with contextual retrieval · platform.claude.com
- StruNRAG: Evaluation of OCR-Induced Structural Noise on RAG Robustness · aclanthology.org
- HiChunk: Evaluating and Enhancing Retrieval Augmented Generation with Hierarchical Chunking · aclanthology.org
- A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges · mdpi.com
- Paper page - Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey · huggingface.co
- Official documentation · qdrant.tech
- Semantic search documentation · elastic.co
- Explore models · huggingface.co
- Research repository · github.com
- Evaluation documentation · docs.ragas.io
Queries
4 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
RAG retrieval optimization noisy documents hybrid search reranking chunking evaluation primary sources 2025 2026 OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
BGE-M3 paper multi-functionality multi-linguality multi-granularity retrieval arxiv OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
BEIR benchmark reranking retrieval evaluation nDCG recall paper OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Anthropic contextual retrieval chunking reranking BM25 vector search OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |