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What is Semantic Similarity Score?
A semantic similarity score measures how closely two pieces of text (like a question and an answer) relate in meaning—regardless of exact wording. In AI systems, it’s used to rank or retrieve the most relevant answers by comparing their vector embeddings. A higher score (closer to 1) means the texts are more alike in context and intent.
Think of it as how well your AI understood what you meant—beyond just matching keywords.
My next RAG POC will use similarity score to distinguish the better approach: the data in vector db or context from the web search agent.
If interested you can find my previous articles on RAG POCs:
A semantic similarity score measures how closely two pieces of text (like a question and an answer) relate in meaning—regardless of exact wording. In AI systems, it’s used to rank or retrieve the most relevant answers by comparing their vector embeddings. A higher score (closer to 1) means the texts are more alike in context and intent.
Think of it as how well your AI understood what you meant—beyond just matching keywords.
My next RAG POC will use similarity score to distinguish the better approach: the data in vector db or context from the web search agent.
If interested you can find my previous articles on RAG POCs: