The development trends of large model products are mainly reflected in the following aspects:
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Finding real demand scenarios is key. It's not just about pursuing technological innovation, but solving actual user problems.
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Independent apps and embedded applications will coexist. Independent apps need more time to accumulate users and scenarios, while embedded applications can quickly implement based on existing ecosystems.
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Product homogenization is severe, requiring differentiated competition. Currently, many AI applications have similar functions and forms; future breakthroughs need to be sought in niche scenarios.
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User data and feedback are crucial. AI products need continuous use to optimize models; obtaining real user data is key.
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Business models are still being explored. How to achieve sustainable profitability is an important challenge facing AI applications.
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Integration with existing business scenarios is a viable path. Relying on mature platform ecosystems can quickly acquire users and application scenarios.
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Personalization and intelligence are development directions. AI capabilities will bring users more intelligent and personalized service experiences.
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Diversified development will be the norm. Different types of AI applications will seek the best path in their respective fields.
Overall, finding suitable implementation scenarios, solving real needs, and establishing sustainable business models are core to the development of large model products. While technological innovation is important, ultimately creating value for ordinary users is key.