This thing is not built for long conversations AT ALL

Challenges in Building a Reliable Database from Repetitive Image Inputs Using AI Tools

Creating an organized database from visual data, such as screenshots of credit card receipts, can seem like a straightforward task—especially with the advancement of AI and large language models (LLMs). However, practical experience reveals that integrating these tools into long-term, repetitive workflows often exposes their limitations.

The Initial Promise

The process began seamlessly. By automating the extraction of data from images and structuring it into a database, the task appeared easy and efficient. The first batch of images—formatted uniformly from a single banking app—led to a smooth, successful operation, resulting in a well-structured, accurate database.

Encountering Limitations Over Time

As the volume of images increased, the AI’s behavior changed unpredictably. Despite sending images in small batches—three at a time—to avoid overloading the system, the AI started suggesting shortcuts that compromised data integrity and occasionally generated blank entries. This inconsistency became increasingly frustrating, especially since the images’ format remained uniform, and the content repetitive.

Persistent Challenges with Repetitive Tasks

The core issue stems from the AI’s handling of repetitive inputs over extended periods. While it can initially process such input effectively, it often “adapts” or “opts out” after multiple iterations, leading to unreliable output. This unpredictability makes it unsuitable for long-term, ongoing data collection tasks that require consistency and accuracy.

Implications for Workflow Design

This experience underscores the importance of understanding a tool’s limitations when planning automation workflows. For tasks involving repetitive data extraction, especially from similar, structured images, it’s crucial to consider whether the AI can sustain consistent performance over time. Otherwise, reliance on such systems could result in incomplete or unreliable databases, ultimately undermining the project’s goals.

Conclusion

While AI and LLM-based tools hold significant promise for automating data extraction and management, they are not yet fully reliable for long-term, repetitive processes. Users should anticipate potential issues with consistency and plan accordingly—either by supplementing AI efforts with manual oversight or exploring alternative automation strategies tailored for high-volume, uniform data inputs.