Almost every website or app provides some form of search functionality these days, with some being clearly more helpful than others. Sometimes you’ll type in exact keywords to find content that you know exists, yet the results tab is empty. Other times you vaguely have an idea of what you’re looking for and use a couple of descriptor words that reveal lists and lists of quality results. Though search tools and enterprise search have been around for decades, search is now, like most technologies, experiencing a developmental leap with the introduction of artificial intelligence.

What did search look like before AI?

There have been numerous tools and platforms over the years, such as Fast ESP and Covnera Retrievalware, that were focused on indexing data and documents, and then retrieving the relevant information for the user. The more advanced the technologies became, the better they got at finding the right information.

Concepts such as pattern matching, taxonomies, semantic search and lemmatisation, among others, became commonplace and helped users find what they were looking for. Semantic search enhances traditional search capabilities by understanding the intent and relationships between words in a query, rather than relying solely on keyword matching. It can recognise synonyms, context and nuances in language, providing precise results that align with what the user is looking for.

What AI brings to the table

It’s all about relevancy. Just as generative AI is revolutionising content creation and taking over menial tasks from people, it’s also being used to improve data and experiences. For example, some applications use semantic search, enhanced with Retrievel-Augmented Generation (RAG) to serve contextually aware results back to the user.

RAG is the process of optimising the output of large language models to reference authoritative knowledges bases outside of its training data sources before generating a response to the user. With that in place, your users will be able to describe the problem, even if they don’t necessarily know the correct keywords, and still receive results relevant to their point of view.

Consider the following scenario: one of your junior team members is trying to set an SSH key for a domain. A typical resolution would probably involve them going to Google to perform a search with the keywords and search clues limited to their understanding of the problem. Eventually, they’ll find a similar-looking problem use case online and try to adapt their findings to their own scenario, and after some troubleshooting and tests, it might just work!

But if the same team member performed that search over a system built with tools like Elasticsearch and AWS Bedrock, they’ll receive detailed instructions in an ordered manner to perform the task, based on the exact problem that they’re describing, with the variables that they have available, referenced from internal and external authoritative knowledge bases, as if they were having a conversation with a more senior colleague.

How semantic search and RAG helps businesses

Improved knowledge management: Semantic search empowers employees by quickly surfacing relevant information from vast internal resources like manuals, knowledge bases and archived documents. Impact: Faster decisions and better collaboration across departments.
Enhanced customer support: AI-powered chatbots and agents use semantic search to access real-time data and historical interactions, resolving customer inquiries efficiently. Impact: Higher first-contact resolution rates and improved customer satisfaction.
Personalised content discovery: Businesses can offer personalised recommendations based on context-aware searches, driving better engagement on websites and apps. Impact: Increased user retention, higher conversion rates and enhanced user experience.
Regulatory compliance and legal review: Semantic search allows legal and compliance teams to identify relevant clauses and documentation quickly, reducing time spent on audits and reviews. Impact: Lower legal risk and improved compliance management.
Proactive issue resolution in IT operations: In IT operations, semantic search helps surface relevant incident reports and solutions, reducing downtime by providing precise troubleshooting steps. Impact: Faster resolution times and improved system reliability.

Why your business needs semantic search and RAG

Traditional search tools often struggle with unstructured data, missing out on context and relationships between data points. Pairing technologies like semantic search and RAG fills that gap, ensuring employees and customers get accurate, relevant information when they need it. By adopting this technology, businesses can transform their operations, delivering better services while improving efficiency and productivity. Solutions like ElasticSearch and AWS Bedrock provide the foundation to deploy and maintain these advanced search systems seamlessly.

With the right semantic search implementation, your business can unlock deeper insights, streamline operations, and provide exceptional customer experiences – transforming the way you use and access data.

For more on this topic, please feel free to engage with the team at LSD Open.