Limited Control Over Timeouts:
External LLM services have factors beyond our control that affect response times, such as server status fluctuations.
Variable LLM Processing Time:
LLMs inherently have unpredictable processing times, especially for complex tasks beyond simple responses, like searching for information in a file.
Other Factors Increasing Complexity:
Uploading data and managing its times, file size limitations, and evolving LLM APIs with potentially limited documentation add complexity to integration.
Mitigation Strategies:
Generous Timeouts:
Account for external factors by implementing more lenient timeouts compared to internal and more “classical” services.
Error Handling:
Carefully monitor and address the various ever changing error messages that may arise during development and testing.
LLM-Specific Challenges:
Data Interaction Issues:
LLMs may encounter errors when reading data files due to their non-deterministic nature (unpredictable steps followed when interacting with a file)
Potential for Hallucination:
LLMs can generate seemingly plausible but inaccurate information ("hallucination") which can be misleading if you do not have a clear idea about the data being consulted.
Gracias por dar luz a temas de IA & LLM, muchas veces quedamos eclipsados por capacidades funcionales que aunque pueden ser realizarse, implementarlas en los negocios con tiempos de respuesta y SLA, pueden no ser aplicables hasta lograr un salto de madurez.