The core of AI search products lies in combining traditional search with large language models to provide more direct and precise answers. The main features include:
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The underlying principle is based on RAG (Retrieval-Augmented Generation), including two main steps: retrieval and generation.
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Most products still rely on traditional search engine APIs for retrieval, while a few build their own index libraries.
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The main innovation is at the product level, rather than the technical level.
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The key is to provide accurate answers, quick responses, and intelligent user experiences.
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Data quality and scale are the biggest barriers, affecting the accuracy and timeliness of answers.
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Building one's own index library can improve accuracy but is costly and technically challenging.
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Currently, there are mainly three types: dedicated AI search products, AI versions of traditional search engines, and search products from large model companies.
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OpenAI's SearchGPT is a specially developed AI search product.
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Competition among AI search products is mainly in user experience and answer quality, rather than underlying technology.
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Business models are still being explored, with cost and monetization being the main challenges.
Overall, AI search is reshaping the search experience but still faces challenges in data, technology, and commercialization.