Blog/Tag

Articles about Embeddings

9 articles tagged Embeddings, newest first. Each one is sourced and written for people who ship.

Retrieval & search
October 17, 2026·9 min read
Choosing an embedding model in 2026: leaderboards, costs and a bake-off on your own data
How to pick an embedding model for RAG in 2026: what MTEB and RTEB tell you, hosted vs open weights, prices, dimensions, and a one-week bake-off.
- Embeddings
- RAG
- MTEB
Retrieval & search
October 17, 2026·10 min read
Contextual retrieval and late chunking: giving RAG chunks back their context
Chunks lose their document. How contextual retrieval, late chunking and contextualized embedding models fix that, what each costs, and when to skip it.
- RAG
- Chunking
- Embeddings
Retrieval & search
October 16, 2026·7 min read
Multilingual RAG for German and Hungarian: embeddings, compounds and cross-lingual search
How to build RAG over German and Hungarian content: multilingual embeddings, compound words, stemming, accents and per-language evals.
- RAG
- Multilingual
- Embeddings
LLMOps & evals
October 16, 2026·6 min read
Semantic caching for LLM apps: when reusing answers is safe, and when it is not
How semantic caches reuse LLM answers for similar questions, how they differ from prompt caching, and how to avoid wrong hits, leaks and stale answers.
- Semantic cache
- LLM cost
- Latency
Retrieval & search
October 16, 2026·5 min read
Embedding quantization: binary, int8 and Matryoshka vectors without losing retrieval quality
How binary and int8 quantization and Matryoshka truncation shrink vector indexes 4 to 32 times, what they cost in recall, and how rescoring wins it back.
- Embeddings
- Vector search
- Quantization
Retrieval & search
October 1, 2026·8 min read
RAG over code: AST chunking, repo maps and code embeddings that fit an agent
How coding agents find code: AST chunking with tree-sitter, repo maps, Merkle-tree index sync, code embedding models in 2026, and when grep beats an index.
- RAG
- Code search
- Tree-sitter
Retrieval & search
September 28, 2026·8 min read
Fine-tuning embedding models for your domain: hard negatives, losses and evals
Fine-tuning embedding models for your domain: when it beats a reranker, hard negatives, sentence-transformers v6 losses, Matryoshka, evals and cost.
- Embeddings
- Fine-tuning
- RAG
Retrieval & search
July 20, 2026·10 min read
Chroma: simple vector search with one catch
Chroma is an Apache-2.0 vector database for embedded, single-node or cloud use. Where it is pleasant, and where its best search is cloud-only.
Retrieval & search
July 9, 2026·11 min read
Pinecone: a review of the managed vector database
Pinecone review: read units scale with namespace size, schemas are fixed at creation, and when self-hosting is the better buy.

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