Embedding models: pick by task, not by hype

Embeddings turn text into vectors for search and clustering. Different models suit different domains.

Some examples

• Voyage and Cohere often beat OpenAI on retrieval benchmarks.
• Open-source (BGE, E5) runs free and locally.
• Test on YOUR data — benchmarks lie.

Try it!

Pick 20 queries from your project. Test 2 embedding models. Pick the winner.

Understanding "Embedding models: pick by task, not by hype" in practice: Understanding AI in this area gives you a real advantage in how you work and think. OpenAI, Voyage, Cohere, and open-source models all do embeddings — best one depends on your use case — and knowing how to apply this gives you a concrete advantage.

• Apply the concepts from Embedding models: pick by task, not by hype directly
• Identify where this fits into your current workflow
• Measure the before/after difference when you apply this
• Iterate and refine — first attempts rarely nail it

1. 1Apply Embedding models: pick by task, not by hype in a live project this week
2. 2Write a short summary of what you'd do differently after learning this
3. 3Share one insight with a colleague