Start by defining embeddings as dense vector representations that capture semantic meaning, then walk through the end-to-end pipeline: data preparation, embedding generation, indexing, query embedding, similarity search, and post-processing. Emphasize trade-offs at each stage, such as model choice, index type, and latency vs. accuracy, to demonstrate system design maturity.
Pro tip: Highlight that embeddings enable semantic search beyond keyword matching, but also discuss challenges like embedding drift, the need for re-indexing, and hybrid approaches combining lexical and vector search for production systems.
Explain that embeddings are dense vectors in a high-dimensional space where similar items are close together, learned from data using neural networks. Mention that they capture semantic relationships and can represent text, images, or other modalities.
Describe how raw data (documents, queries) is preprocessed (tokenization, normalization) and then passed through an embedding model (e.g., transformer-based) to produce vectors. Discuss choices like model size, dimensionality, and fine-tuning.
Explain how embeddings are stored in a vector database or index (e.g., FAISS, Annoy, HNSW) to enable efficient nearest neighbor search. Mention trade-offs between exact and approximate search, and considerations for scalability and updates.
Walk through how a user query is embedded using the same model, then used to search the index for nearest neighbors. Discuss similarity metrics (cosine, dot product) and how to retrieve top-k results.
Describe how retrieved candidates may be re-ranked using additional signals (e.g., cross-encoders, business rules) and how results are presented. Mention evaluation metrics like recall@k and latency considerations.
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