Introducing RTEB: A New Standard for Retrieval Evaluation

What changed

TL;DR – We’re excited to introduce the beta version of the Retrieval Embedding Benchmark (RTEB), a new benchmark designed to reliably evaluate the retrieval accuracy of embedding models for real-world applications. Existing benchmarks struggle to measure true generalization, while RTEB addresses this with a hybrid strategy of open and private datasets. Its goal is simple: to create a fair, transparent, and application-focused standard for measuring how models perform on data they haven’t seen before.

Why it matters

A concrete addition to Practical AI: Tools, Models & Frameworks: it changes what's available to builders today rather than being general commentary.

How it compares

Related prior coverage to compare against:

• The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix
• Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval
• Introducing HealthBench

• The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix similarity 0.22
• Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval similarity 0.17
• Introducing HealthBench similarity 0.17