5 AI search trends impacting developers in 2024
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After an incredibly fast-moving 2023, what does the future hold for AI and search? Conversational generative AI leapt into the public consciousness over the past year, and organizations scrambled to define their strategy for capitalizing on the trend. AI-boosted relevance is reshaping the way users experience search — and elevating their expectations for the quality of the interaction.
In a recent discussion with product and engineering leaders from Adobe, Hugging Face, You.com, and Elastic, we assessed the current state of search and weighed in on these AI-fueled trends. Here are a few key themes they see on the horizon for all things AI in 2024:
What are the top trends for search and AI in 2024?
1. Rapidly evolving user expectations for search
Keyword-based search with pages and pages of results is so 2023. In 2024 and beyond, users expect experiences that better understand natural language queries and provide direct answers. Vector search and LLMs are helping developers create reimagined search capabilities where lists of links are being replaced with a prescriptive answer, including interactive conversational experiences. Answers, not blue links, is the direction search is headed.
2. Data safety and privacy
Data safeguards, privacy policies, and sound processes for data provenance and governance have always been a priority in the past, but they’ve become increasingly important in the age of AI. The legal implications, intellectual property considerations for training models, organizational data safekeeping practices, and user protections will all be hot-button issues.
The ethical dilemma that we all know for AI is that it should not be biased, so you try to get reliable sources that you train your data on. But then these reliable sources, as evidenced by the New York Times lawsuit, come at a cost. And how do we standardize this? If the numbers are to be believed, the cost of licensing those articles is actually going to be way more than the cost of generating foundational models.
Fahd Siddiqui, Director of Engineering, Adobe Commerce
3. Proper context for generative AI
The generative capabilities of LLMs are truly remarkable. Yet, hallucinations, outdated training data, and lack of proper context present challenges in standing up truly helpful interactive experiences. Improvements to AI text generation with techniques like retrieval-augmented generation (RAG) can provide LLMs with critical domain-specific context from proprietary data.
. . . what retrieval augmented generation does is ground these LLMs and provide additional context to help it answer the user's information need. I think RAG continues to be a big trend in 2024.
Raj Arasu, Senior Software Engineer, You.com
4. The next wave of LLMs
2024 is likely to bring another barrage of new language models, open data sets, and machine learning applications. Expect many models that are even more efficient and more adapted to all kinds of specific business problems.
5. AI expertise
What are the skill sets needed for an AI-powered future — and what are the tools that will complement those skills? A strong grounding in foundational AI knowledge is obviously important. But with the dizzying advances in AI along with scores of new tools, so is a continuous learning mindset.
...to really fulfill the promise of AI, we really need to reach developers and empower them, give them the tools to build their own AI without having to either get a formal degree in deep learning or having to rely on external services that are a complete black box to their systems.
Jeff Boudier, Head of Product and Growth, Hugging Face
Tune in for more
Want to explore these trends further? Whether you’re a seasoned search developer keen on staying ahead of the curve, or an AI enthusiast eager to explore what's next, join our virtual event for more valuable insights, practical tips, and a deeper understanding of the technical trends that are shaping search and the ecosystem in 2024.
You’ll hear from our expert panel of industry guests, each with a unique perspective on designing and building world-class search (and the tools that make those experiences possible):
Learn the different methods for building AI-search applications.
The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.
In this blog post, we may have used or referred to third party generative AI tools, which are owned and operated by their respective owners. Elastic does not have any control over the third party tools and we have no responsibility or liability for their content, operation or use, nor for any loss or damage that may arise from your use of such tools. Please exercise caution when using AI tools with personal, sensitive or confidential information. Any data you submit may be used for AI training or other purposes. There is no guarantee that information you provide will be kept secure or confidential. You should familiarize yourself with the privacy practices and terms of use of any generative AI tools prior to use.
Elastic, Elasticsearch, ESRE, Elasticsearch Relevance Engine and associated marks are trademarks, logos or registered trademarks of Elasticsearch N.V. in the United States and other countries. All other company and product names are trademarks, logos or registered trademarks of their respective owners.
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