How to Ensure Reliability in LLM Applications

Author(s): Eivind Kjosbakken

Originally published on Towards AI.

Learn how to make your LLM applications more robust

LLMs have entered the world of computer science at a record pace. LLMs are powerful models capable of effectively performing a wide variety of tasks. However, LLM outputs are stochastic, making them unreliable. In this article, I discuss how you can ensure reliability on your LLM applications by properly prompting the model and handling the output.

In the article, the author discusses the rapid emergence of LLMs and the inherent unreliability of their outputs due to stochastic behavior. They outline strategies for ensuring reliability in LLM applications, including implementing consistent prompting techniques, handling errors effectively, and using output validation tools. The author also emphasizes the importance of having backup LLM systems in place and making use of markup tags to improve overall consistency in responses.

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Published via Towards AI