Ever wondered how Google differentiates between searches for “apple” as a fruit and “Apple” as a tech company? The answer lies in semantic search, a technology that understands the context and intent behind search queries. By using natural language processing (NLP) and machine learning (ML), Google interprets subtle differences and delivers relevant results, rather than simply matching words.
For businesses, semantic search can transform customer interactions. By integrating semantic search with Retrieval Augmented Generation (RAG), companies can deliver more accurate answers to customers’ questions. According to McKinsey, AI automation in customer support can boost satisfaction by 20%.
This article will guide you through the technical details and practical benefits of semantic search for enterprises, and show you how Voiceflow can help you leverage this technology to build custom AI agents that enhance customer service.
Semantic search has evolved significantly from basic keyword matching to advanced algorithms that can understand context and intent.
Initially, search engines relied on simple string matching, for example:
→ Document 1: Discover the apple fruit benefits for your health
→ Document 2: Apple fruit benefits are significant for your health
However, this means that documents like:
→ Document 3: Health benefits of apple include improved heart health
→ Document 4: Nutritional value of apples: benefits for the body
will be overlooked because they don’t contain the exact phrase.
Semantic vectors, or word embeddings, are mathematical representations of words in a continuous vector space where similar words are close together. These vectors capture the meanings and relationships of words based on how they are used in large amounts of text. Semantic vectors enhance keyword search in a few ways:
The development of natural language processing (NLP) and machine learning in the 2000s improved search capabilities through techniques like word embeddings (aka semantic vectors such as Word2Vec and GloVe) into dense vector representations in high-dimensional space to capture semantic relationships.
The major breakthrough in semantic search came with the advent of transformers, particularly the BERT (Bidirectional Encoder Representations from Transformers) model introduced by Google in 2018. BERT processes words in relation to all other words in a sentence, allowing for a better understanding of context and intent. Transformers utilize self-attention mechanisms to weigh the importance of each word, enabling the model to focus on relevant parts of the text. These advancements have made it much easier to understand what uses are really looking for, leading to more accurate and relevant search results.
Retrieval-Augmented Generation (RAG) combines searching for information with creating responses:
For example, if you ask, “What is Voiceflow?” a RAG system would first retrieve information from various sources, such as news articles, about Voiceflow. It would then use this information to generate a clear and accurate response like, “Voiceflow is a platform that allows users to design, prototype, and build conversational interfaces for voice and chat applications without needing to write code.”
Semantic search improves RAG because it can identify and prioritize documents that explain what Voiceflow is. This allows the retrieval system to find the most relevant and contextually appropriate information.
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Enterprises are embracing AI technologies such as semantic search at an unprecedented pace, with studies showing that over 80% will integrate generative AI by 2026, up from just 5% in 2023. This rapid adoption is revolutionizing business operations, from product development to customer service:
Here are the top 3 tips if your company wants to implement semantic search:
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Semantic search is truly changing how businesses interact with customers and retrieve information. Using advanced ML algorithms, it understands the intent behind queries and delivers highly accurate results.
Enterprises can take advantage of this technology to transform customer support, boost productivity, and enhance user satisfaction. With tools like Voiceflow, which combines semantic search with large language models and Retrieval-Augmented Generation (RAG), companies can create custom knowledge bases and deliver precise answers through AI customer support agents instantly. Get started today—it’s free!
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When a user makes a voice query, semantic search uses natural language processing (NLP) and machine learning (ML) to understand the context and meaning behind these spoken words. This allows the search engine to interpret voice queries more effectively and deliver more precise answers. Voiceflow empowers anyone to build an AI-powered voice assistant that uses semantic search to retrieve answers from a custom knowledge base, for free.
When semantic loss happens, it makes the information less clear and relevant, causing confusion and less effective communication within the system.
Semantic search makes customer service automation smarter by giving more accurate and context-aware responses, which keeps customers happy and saves time. You can build your own AI customer service agent from scratch in 15 minutes using Voiceflow.
Semantic search helps manage knowledge in enterprises by quickly finding the right information in large datasets, making it easy for employees to get what they need.
BERT (Bidirectional Encoder Representations from Transformers) is a smart language model , developed by researchers at Google in 2018, that makes semantic search better by understanding the context of words in a sentence, leading to more accurate and meaningful search results.
Want to explore how Voiceflow can be a valuable resource for you? Let's talk.
