Building LLM-based applications can undoubtedly provide valuable solutions for several problems. However, understanding and proactively addressing challenges such as hallucinations, prompt context, reliability, prompt engineering, and security will be instrumental in harnessing the true potential of LLMs while ensuring optimal performance and user satisfaction. In this article, we will explore these five crucial considerations that developers and practitioners should know when building LLM applications.

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1. Hallucinations

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One of the main aspects that you should take care of when using LLMs is hallucinations. In the context of LLMs, hallucinations refer to generating unreal, incorrect, nonsensical information. LLMs are very creative and they can be used and tuned for different domains but a very critical unsolved problem that still exists is their hallucinations. Since the LLMs are not search engines or databases, therefore these mistakes are unavoidable. 

To overcome this problem you can use controlled generation by providing enough details and constraints for the input prompt to limit the model's freedom to hallucinate. 

2. Choosing The Proper Context

As mentioned one of the solutions to the hallucinations problem is providing the proper context to the input prompt to limit the LLM's freedom to hallucinate. However, on the other hand, LLMs have a limit on the number of words that can be used. One possible solution for this problem is using indexing in which the data is turned into vectors and stored in a database and the appropriate content is searched during runtime. Indexing usually works however it is complex to implement. 

3. Reliability And Consistency

One of the problems you will face if you build an application based on LLM is reliability and consistency. LLMs are not reliable and consistent to make sure that the model output will be right or as expected every time. You can build a demo of an application and run it multiple times and when you lunch your application you will find that the output might not be consistent which will cause a lot of problems for your users and customers. 

4. Prompt Engineering Is Not the Future 

The best way to communicate with a computer is through a programming or machine language, not a natural language. We need an unambiguous so that the computer will understand our requirements. The problem with LLMs is that if you asked LLM to do a specific thing with the same prompt ten times you might get ten different outputs. 

5. Prompt Injection Security ProblemA

Another problem you will face when building an application based on LLMs is prompt injection. In this case, users will enforce the LLMs to give a certain output that isn’t expected. For example, if you created an application to generate a youtube script video if you provide a title. A user can instruct to forget everything and write a story. 

Wrap UP

Building an LLMs application is a lot of fun and can solve several problems and automate a lot of tasks. However, it comes with some issues that you take care of when building LLMs-based applications. Beginning from hallucinations, choosing the right prompt context to overcome the hallucinations and output reliability and consistency and the security concerns with prompt injection. 

References 

• A Gentle Introduction to Hallucinations in Large Language Models
• 5 problems when using a Large Language Model

 
 
Youssef Rafaat is a computer vision researcher & data scientist. His research focuses on developing real-time computer vision algorithms for healthcare applications. He also worked as a data scientist for more than 3 years in the marketing, finance, and healthcare domain.