Voyage Code

Benchmarking semantic code retrieval on Claude Code — Kuba Rogut, Turbopuffer

Jun 3, 2026 · 16:08

Kuba Rogut from Turbopuffer benchmarks semantic code retrieval on Claude Code, finding that raw Claude Code wastes one in every three file reads (65% precision), windowed grep drops that to one in five, and adding semantic search cuts it to one in eight (87% precision). Semantic search wins on behavior-adjacent tasks where files share no keywords; grep wins on import tracing. Cursor's production numbers show a 24% relative improvement in answer accuracy from semantic retrieval plus a 2.6% increase in code retention in large codebases. Rogut explains Cursor's model knows when and why to call semantic search, while Claude Code treats it as just another tool, limiting gains.

The State of AI Powered Search and Retrieval — Frank Liu, MongoDB (prev Voyage AI)

Jun 27, 2025 · 12:35

Frank Liu from Voyage AI (now part of MongoDB) presents the state of AI-powered search and retrieval, arguing that embedding quality is core but not sufficient—systems must integrate structured data filtering and agentic query decomposition for real-world RAG applications. He details three live use cases: chatting with codebases (where Continue.dev found Voyage Code 3 best, emphasizing domain-specific evaluation), legal document search requiring pre-filtering by metadata, and agentic retrieval where LLMs decompose queries into multiple sub-queries. Looking ahead, Liu predicts three trends: multimodal embeddings that combine text and images (e.g., Voyage Multimodal 3 embedding Claude 3.5 blog posts), instruction tuning to steer embeddings toward specific document aspects, and the rise of agent-native databases (like MongoDB integrating Voyage for embedding, reranking, and query augmentation within a single platform).