Search Isn’t Dead: From RAG to Agents With Vector Databases

We’re so glad you’re here. You can expect all the best TNS content to arrive Monday through Friday to keep you on top of the news and at the top of your game. Check your inbox for a confirmation email where you can adjust your preferences and even join additional groups. Follow TNS on your favorite social media networks. Become a TNS follower on LinkedIn. Check out the latest featured and trending stories while you wait for your first TNS newsletter. Note: This article was originally published on Oct. 16, 2025, and has been updated with information from the webinar.In the not-so-distant past, search was relatively simple: A search engine like Google would find and rank content that matched the keywords or phrases of a query. But this traditional model of lexical search has been shattered by the emergence of large language models (LLMs).And it’s happened very quickly. As Dave Moore, principal solutions architect at Elastic, explained in a recent webinar with The New Stack, “for the longest time, search was the technology to beat” — to the point where we’ve taken it for granted.However, in December 2024, just two years after ChatGPT publicly launched, research by Future found “in the US, at least, about a quarter of the respondents had said that they use AI tools instead of search engines as their primary means of searching for information.”Today, semantic, generative AI (GenAI) and agent-based approaches are reshaping industry practices by allowing people to ask complex questions using natural language. However, these approaches still struggle with issues like the accuracy of results and hallucinations, citation quality, and the freshness of information.GenAI is also altering the technologies underlying search. This is especially true for databases, as the traditional relational databases that have long powered search aren’t well equipped to handle new approaches like retrieval augmented generation (RAG).Now vector databases are taking the lead, enabling trustworthy, intent-driven retrieval and powering reliable AI agents. These capabilities help ensure accuracy, adaptability and seamless integration with modern AI frameworks.They also enable people to search using images — not just natural language, which Moore demonstrated during the webinar, Achieving Relevant AI with Vector Search, now available on demand.Watch now to learn about the critical role of vector databases in enabling trustworthy, intent-driven retrieval and powering reliable AI agents.By attending this special online event, you’ll leave with best practices, real-world examples and actionable tips, including: Community created roadmaps, articles, resources and journeys for developers to help you choose your path and grow in your career.