Explain Embeddings and Cosine Similarity.

Embeddings convert data (like text or images) into numeric vectors, and cosine similarity measures the angle between these vectors to determine how similar two items are in meaning.

When to Use

Use embeddings with cosine similarity when you need semantic comparison instead of exact matches.

Common use cases include:

• Search engines (finding meaning, not just keywords)
• Recommendation systems (spotting related content)
• Clustering/grouping (organizing similar data points)

Example

Think of two book summaries. Even if they don’t share many words, embeddings capture meaning, and cosine similarity finds their closeness by angle, not exact wording.

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Why Is It Important

This pairing powers modern AI systems by enabling semantic search and context-aware recommendations, far beyond keyword matching.

Interview Tips

Define embeddings as vector representations and cosine similarity as an angle-based similarity measure. Use analogies (like comparing directions rather than distances) to sound clear and confident.

Trade-offs

You gain nuanced semantic matching but at the cost of higher computation and complexity in managing high-dimensional vectors.

Pitfalls

Avoid assuming embeddings work without quality training data, forgetting to normalize vectors, or over-trusting scores without context.