Core

Choosing and Adapting Embedding Models

Picking an embedding model is a decision about retrieval quality, cost, and operational risk on your data, not about who tops a public leaderboard. The hard parts are benchmarking on your own queries, trading dimensionality against storage and latency, deciding whether to fine-tune for your domain, and planning for the re-embedding migration when the model changes. Applied AI interviews probe it because candidates default to the leaderboard winner and ignore the drift and migration costs that bite later.

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RELATED CONCEPTS

Embeddings Vector Search and ANN Indexes RAG Evaluation Chunking

PRACTICE THIS IN REAL QUESTIONS

RAG & Agent System DesignDesign a production RAG system over 10M documents serving ~1,000 QPS at sub-second latency.→System Design for AI in ProductionDesign a multimodal (text and image) search system for a large e-commerce catalog.→RAG & Agent System DesignWhat is HyDE (Hypothetical Document Embeddings), and why does it improve retrieval?→RAG & Agent System DesignWhat is semantic chunking, and how does it compare to fixed-size chunking?→RAG & Agent System DesignOff-the-shelf embeddings retrieve poorly on your domain. How do you improve retrieval accuracy?→LLM & GenAI FundamentalsWhat is RAFT (Retrieval-Augmented Fine-Tuning), and how does it combine RAG and fine-tuning?→

COMPANIES THAT ASSUME THIS

OpenAI Anthropic Google Microsoft Scale AI Cognition

NEXT IN RETRIEVAL & AGENTSChunking→