Asking questions instead of keywords
Generative AI was always going to upend search. It’s a technology that can find answers to virtually any question posed to it. And in the process of changing the world of search, AI developers latched on to something else that would meld search and generative AI even more.
Generative AI has changed three essential aspects of search: how people ask and look for information, how to get data for answers and how companies can start offering this information to customers. Venture Beat.
The VentureBeat article discusses how generative AI is transforming the search landscape. It focuses on three main aspects: changes in search behavior, the impact on traditional search engines, and the rise of Retrieval Augmented Generation (RAG). Here are the key points from the article:
Shift in search behavior:
Generative AI, especially large language models like ChatGPT, enables users to ask questions in natural language instead of relying on keyword-based queries. This change allows users to receive direct answers without the need to navigate through multiple websites.
AI company Perplexity took advantage of this shift in search methods and positioned itself as more of a search engine than a chatbot that can generate code or art. The company partnered with data providers like Yelp and Wolfram Alpha to gather data better. The strategy has worked. VentureBeat reported that Perplexity’s platform has grown in traffic referrals. Venture Beat.
Impact on traditional search engines:
Google’s dominance in search (accounting for 82% of search traffic) is being challenged. The need for traditional SEO strategies may decrease as AI can interpret and answer questions directly.
For years, Google has dominated Search. As the dominant search engine (with almost 82% of search traffic), it dictated how users and customers look for information and how brands show up in results. Companies had to lean into search engine optimization (SEO) strategies, and people constructed queries into a keyword salad. It didn’t always yield good results, but it was passable, and everyone learned to translate their questions into keywords and interpret which of the lists of websites on the results page might have what they were looking for.
Large language models (LLM) changed that, especially when deployed in chatbots like OpenAI’s ChatGPT. People could suddenly ask any question they wanted (within guardrail reason) and get an answer right back. There’s no need to click through a series of websites; it’s all explained to you. Venture Beat.
Retrieval Augmented Generation (RAG):
RAG, a significant trend in generative AI, allows companies to ground AI models in their own data. This ensures that results come from a company’s internal documents, making it particularly useful for customer support and internal use cases. Essentially, RAG allows AI models to incorporate a company’s data, providing more accurate and relevant responses in specific contexts.
Future of search:
AI is undoubtedly reshaping the search landscape. However, it’s crucial to understand that this transformation doesn’t spell the end of traditional search engines. Instead, we’re likely heading towards a more nuanced and decentralized search ecosystem.
Consider this scenario mentioned in the article:
-
- For general queries like finding the nearest pizza shop, traditional search engines like Google will continue to be our go-to resource.
- However, for more specific, detailed information – such as allergen information for a particular restaurant – we may increasingly turn to AI-powered tools provided by the businesses themselves.