Lesson 1.3
Similarity metrics and thresholds
Cosine similarity is the usual default, but the number it gives you is only meaningful relative to your own corpus. Calibrate the threshold, do not guess it.
Taught byPriya RamanLLMs · RAG · Prompt engineering · Evaluation
Overview
Cosine similarity is the usual default, but the number it gives you is only meaningful relative to your own corpus. Calibrate the threshold, do not guess it.
What this lesson covers
- Compare cosine, dot product, and euclidean distance
- Calibrate a relevance threshold on real queries
- Detect the case where nothing is actually relevant
This lesson sits in Embeddings and Vector Search, part of Retrieval-Augmented Generation from Scratch. It assumes what came before it and leads directly into the next lesson in the module.
In this lesson you will:
- Compare cosine, dot product, and euclidean distance
- Calibrate a relevance threshold on real queries
- Detect the case where nothing is actually relevant