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What is an embedding?
An embedding is the representation of content—usually text—as a multidimensional numerical vector. Similar content is clustered closely together in this space, while unrelated content is far apart.
Embeddings are generated by specialized embedding models —such as text-embedding-3 (OpenAI), Cohere Embed, BGE, or E5. These models are trained to translate semantic similarity into vector distances.
In production AI applications, embeddings form the basis of semantic search: Instead of comparing words, the query vector is compared with stored chunk vectors. The semantically most similar chunks are returned.
For businesses, embeddings are therefore essential for making knowledge available in AI applications —from RAG-based chatbots to similarity analyses for sales and support.
Best Practices for Using Embeddings
To ensure that embeddings work reliably in everyday use, these six principles are worth following:
- Test your chunking strategy: Chunk size, overlap, and semantic separation are more important than the embedding model itself.
- Store metadata: Source, author, date, rights—essential for filtering, reranking, and compliance.
- Use hybrid search: Combine vector search with traditional text search—significantly better results than either method alone.
- Incorporate a reranker: A second model narrows down 20 candidates to the top 5—the ROI is usually enormous.
- Measure retrieval quality: Without evaluations, poor retrieval goes unnoticed—and the entire RAG system delivers poor results.
- Respect permissions: Filters in the retrieval process ensure that users only see what they’re authorized to see.
Common Mistakes in Embeddings
These mistakes make embedding pipelines worse than they need to be:
- Inappropriate chunk size: Too small → no context. Too large → hit precision suffers. Experiment and measure.
- Wrong language model: An English model works only moderately well on German data — check the language.
- Using old embeddings: New model = new vector space. Old vectors are then incompatible.
- No reranking stage: Without a reranker, the top results are often just “similar,” not “correct.”
- Metadata ignored: Without filters, outdated or rights-infringing content makes its way into responses.
Embedding vs. Fine-Tuning vs. Prompt Engineering
Three tools that are often compared—with different purposes:
- Embedding: Making knowledge accessible. The foundation of RAG and semantic search.
- Prompt engineering: Control model behavior via the prompt. Fast and cost-effective.
- Fine-Tuning: Retraining the model itself using your own data. For very specific tasks.
Using Embeddings for Your Company's Knowledge
In a free initial consultation, we’ll review your data sources and show you how to make your corporate knowledge accessible using embeddings and semantic search.
As an AI partner for small and medium-sized businesses, we’ll take your embedding pipeline from prototype to production—including reranking, monitoring, and evaluation.
What We Offer
- RAG Consulting & Implementation — Embeddings as the foundation for productive AI solutions.
- AI Integration — Seamless integration with SharePoint, DMS, ERP, and CRM.
- AI Workshop — Selection of model, chunking, and vector store.
- Vector Database in the Glossary — the right storage layer for your embeddings.
