{"id":27566,"date":"2025-05-27T22:46:39","date_gmt":"2025-05-28T05:46:39","guid":{"rendered":"https:\/\/www.pingcap.com\/?p=27566"},"modified":"2025-11-14T06:41:03","modified_gmt":"2025-11-14T14:41:03","slug":"why-fresh-data-sql-and-unified-platforms-are-your-competitive-edge-for-ai-applications","status":"publish","type":"post","link":"https:\/\/www.pingcap.com\/ko\/blog\/why-fresh-data-sql-and-unified-platforms-are-your-competitive-edge-for-ai-applications\/","title":{"rendered":"Why Fresh Data, SQL, and Unified Platforms Are Your Competitive Edge for AI Applications"},"content":{"rendered":"

Delivering truly personalized AI experiences is complex. It requires Large Language Models (LLMs) to access relevant personal or organizational information and possess the intelligence to translate needs into tangible actions. This pursuit has been a key direction in GenAI application<\/a> development.<\/p>\n\n\n\n

With foundational models like GPT-4o and DeepSeek-V2 becoming genuinely practical, their reasoning and execution capabilities have reached a usable state. This largely explains why many are calling 2024\/2025 the “Year of the AI Agent.” However, a model’s inherent ability is only half the story. Crucially, an Agent’s effectiveness hinges on the context<\/strong> it’s given. That\u2019s the core of our discussion today.<\/p>\n\n\n\n

Before diving into data foundations, let me share my perspective on AI-native applications<\/strong> (e.g., agents or assistants):<\/p>\n\n\n\n

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  1. Flexibility:<\/strong> They must understand and act on relatively ambiguous inputs, unlike traditional software needing precise commands.<\/li>\n\n\n\n
  2. Personalized Context:<\/strong> Their interactions and outputs must be deeply rooted in specific data and background about an individual or organization.<\/li>\n\n\n\n
  3. Action-Oriented (Tool-Use Enabled):<\/strong> They need the ability to call external tools, impacting the real world, often via frameworks like the Model Context Protocol (MCP).<\/li>\n<\/ol>\n\n\n\n

    Consider the common executive challenge: endless briefings and a constant hunt for specific figures or project updates, often lost in a sea of documents. It’s tedious for everyone involved. You might need a single data point, but unearthing it can be a major task for your team. Historically, this necessitated dedicated assistants and data analysts.<\/p>\n\n\n\n

    Now, with smarter LLMs, we have an opportunity for change. As the CTO of a database company and an avid developer, my first personal project was, naturally, a digital personal assistant. Tinkering with this “toy” provided valuable insights into building data foundations for AI-native applications.<\/p>\n\n\n\n

    When designing such knowledge-based AI applications<\/a>, many\u2014including myself initially\u2014think of RAG (Retrieval Augmented Generation)<\/a>. Practice, however, revealed a more nuanced reality. Here are some key takeaways:<\/p>\n\n\n\n

    <\/span>1. Beyond Big Data: The Primacy of Fresh, Raw Data<\/span><\/h2>\n\n\n\n

    In scenarios demanding rapid responses and decision support, raw, event-level data<\/strong> is far more valuable than its vectorized shadow. Why? We often care most about specific details, up-to-the-second data freshness (e.g., a critical customer issue this week versus last year’s satisfaction trends), and subtle nuances lost in aggregation.<\/p>\n\n\n\n

    Traditional “Big Data” systems, designed for large-scale batch analytics, catered to human limitations in processing raw information. AI Agents, however, can sift through and connect vast, disparate, yet incredibly fresh data points in real-time. If AI can directly access this primary data, our reliance on pre-digested, summarized reports diminishes. My digital assistant, for instance, mines insights from raw emails and JIRA tickets, not pre-prepared presentations. Vectorization is useful, but not the sole solution. In RAG, without access to original data for verification, LLMs are prone to confident “hallucinations.” Industries like HealthTech (relying on real-time epidemiological data) and LegalTech (tracking new case law) have an almost ruthless demand for data freshness.<\/p>\n\n\n\n

    This means your data platform must excel at ingesting and, more importantly, enabling AI Agents to efficiently access massive, continuous streams of fine-grained business data in real-time.<\/p>\n\n\n\n

    <\/span>2. SQL: More Relevant Than Ever as the Human-AI-Data Bridge<\/span><\/h2>\n\n\n\n

    Contrary to notions of its decline, <\/strong>SQL’s importance is amplified in the AI era. <\/strong>Here\u2019s how:<\/p>\n\n\n\n