Identifying the Limits of AI — 3 Technical Reasons Why Engineers Handling “Raw Japanese Data” Must Return to APIs

“With Generative AI, we no longer need structured databases or strict APIs”—such was the whisper following the rise of Large Language Models (LLMs). However, on the front lines of production, particularly when dealing with the complex and idiosyncratic nature of “raw Japanese data,” this illusion quickly shatters.

Entrusting the accuracy of information entirely to LLMs like ChatGPT is, in a production environment, akin to operating with unexploded ordnance in your stack. Drawing on the perspective proposed by the prominent engineer Qrara, we will take a deep dive from a tech evangelist’s viewpoint into why we must now re-evaluate and build “deterministic APIs.”

【Tech Watch Reflection】 An LLM is a machine that spins "the word most likely to come next"; it is not a database where facts are stored. It is inherently incompatible with "structured data"—such as the Japanese address system or national holidays—that changes dynamically based on administrative decisions and requires absolute precision. Engineers well-versed in the latest AI trends must calmly identify these "physical limits of AI" and reposition the traditional, robust solution of the API at the core of their architecture.

1. ChatGPT’s “Three Achilles’ Heels of Hallucination”

Why does the seemingly omnipotent ChatGPT fail to avoid “hallucinations” (plausible-sounding lies) in specific data domains? Let’s examine this in the context of the complexities unique to Japanese data.

① The Asymmetry of Postal Codes and Addresses

Japanese postal code data is updated almost every month. From municipal mergers to the addition of building names due to large-scale redevelopments, the master data published by Japan Post is extremely fluid. An LLM’s training data is merely a “snapshot” taken at a specific point in time. Asking a model without real-time capabilities for the latest address is like walking through a labyrinth with an outdated map. In fields where a single delivery error is unacceptable—such as e-commerce logistics—this lag of even a few months can lead to fatal business losses.

② “Japanese Holidays” Dependent on Legislation

The question “When is the substitute holiday next year?” is a complex puzzle for an AI. Japanese holidays are defined by the “Act on National Holidays,” but the logic is highly dynamic, involving the “Happy Monday System,” calculations for substitute holidays (furikae kyujitsu), and special measures for international events. While AI performs inference based on past patterns, it cannot foresee special measures announced by the government at the last minute. Relying on non-deterministic inference for core functions like calendar reservation systems is extremely dangerous.

③ Notation Fluctuations and Address Normalization

“1-chome 2-ban 3-go,” “1-2-3,” “一丁目二番三” (Kanji numerals)… The chaos of “notation fluctuation” in Japanese addresses has plagued developers for years. While ChatGPT can flexibly interpret these in a conversational context, integration with GIS (Geographic Information Systems) or conversion to latitude and longitude requires unambiguous “normalization.” Reliable normalization requires a strict rule-based engine or a constantly updated reference API.

2. Why “AI + RAG” Cannot Surpass the Reliability of “Dedicated APIs”

In recent years, Retrieval-Augmented Generation (RAG)—which searches for external knowledge to incorporate into answers—has become widespread. However, when it comes to the single point of “returning an accurate value,” dedicated APIs still hold the advantage due to the tradeoff between “deterministic behavior” and “computational cost.”

An API returns a response with 100% accuracy (or an explicit error) for a defined request within milliseconds. In contrast, processing via an LLM is always “probabilistic,” and validating the output consumes further computational resources. The essence of engineering lies in controlling uncertainty. Any architect of large-scale systems intuitively understands the danger of embedding non-deterministic behavior into core components.

3. Best Practices for Building a Robust Data Foundation

To enjoy the convenience of AI while guaranteeing system reliability, the following engineering approaches are necessary:

  • Automated Data Pipelines: Build pipelines into your CI/CD that periodically fetch data from primary sources (like Japan Post) and update your own database. Eliminating manual updates is the only way to maintain data freshness.
  • Optimization of Caching and TTL: Address data is relatively static, but holidays undergo major changes once a year. You must set appropriate TTL (Time To Live) values based on the data’s characteristics to balance performance and consistency.
  • Adopting a Hybrid Architecture: Accept flexible input via AI at the frontend interface, but always pass backend processing through “API-based validation.” The optimal solution is to assign AI the role of “Translator” and the API the role of the “Single Source of Truth (SSOT).”

FAQ: Key Points in Architectural Selection

Q: Aren’t commercial services like the Google Maps API sufficient? A: Commercial APIs are indeed powerful. However, the costs that increase in proportion to the number of requests cannot be ignored. When specializing in a specific domain (e.g., Japanese addresses only), adopting a lightweight self-made API or a specialized domestic API can drastically improve long-term ROI (Return on Investment).

Q: Is it possible that LLMs will solve this problem in the future? A: Real-time search functions like SearchGPT are evolving, but “accurate extraction from structured data” has yet to overcome the wall of probability. An API is “Logic,” whereas AI is “Context.” This division of roles is unlikely to change fundamentally, even as technology evolves.

Conclusion: In the Age of AI, “Reliable Data” Becomes the Strongest Asset

The true competency required of the next generation of engineers is not to entrust all processing to AI, but to elegantly distinguish between “unstructured/creative processing where AI excels” and “structured/deterministic processing where APIs excel.”

The three data domains mentioned here represent the exact boundary between AI and APIs. Being conscious of this boundary and building a solid data foundation—it is this steady accumulation that elevates a product’s reliability to something unshakable.

Let us not be swallowed by the wave of technology, but instead build the “solid ground” needed to control that wave.

This article is also available in Japanese.