5 Challenges Come with Building LLM-Based Applications
Developing LLM-based applications can offer effective solutions for various issues. However, it’s important to acknowledge and tackle potential challenges such as hallucinations, contextual promptness, dependability, efficient engineering, and security in order to fully utilize the potential of LLMs and ensure satisfactory performance for users. In this article, we will delve into these five critical factors that developers and practitioners must consider when creating LLM applications.
Table of Contents:
Hallucinations
Choosing The Proper Context
Reliability And Consistency
Prompt Engineering Is Not the Future
Prompt Injection Security Problem
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1. Hallucinations
When using LLMs, it’s important to be aware of the risk of hallucinations. This refers to the generation of inaccurate and nonsensical information. Though LLMs are versatile and can be tailored to various domains, hallucinations remain a significant issue. As they aren’t search engines or databases, such errors are inevitable. To mitigate this, you can employ controlled generation by offering specific details and constraints for the input prompt, which will restrict the model’s ability to hallucinate.
2. Choosing The Proper Context
One of the problems you will face if you built an application that is based on LLM is reliability and consistency. LLMs are not reliable and consistent enough 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.
3. Reliability And Consistency
The challenge of “Reliability and Consistency” in building LLM-based applications involves ensuring that the generated content is accurate, unbiased, and coherent across different interactions. There are several issues that contribute to this challenge:
Bias and Inaccuracies: LLMs can unintentionally produce biased or incorrect information due to biases in their training data.
Out-of-Distribution Inputs: When faced with inputs that differ significantly from their training data, LLMs may generate unreliable responses.
Fine-tuning Issues: Improper fine-tuning can lead to inconsistencies and errors in LLM-generated content.
User Expectations: Users expect consistent and reliable behavior from applications, and inconsistency can erode trust.
Lack of Ground Truth: Language nuances make it challenging to determine a single “correct” response for every input.
Addressing this challenge involves:
Using diverse and high-quality training data to reduce biases and improve accuracy.
Applying bias mitigation techniques during fine-tuning or post-processing.
Incorporating human oversight to validate outputs and catch issues.
Creating feedback loops for users to report problematic content.
Regularly monitoring performance and making iterative improvements to enhance reliability and consistency.
In essence, maintaining reliability and consistency in LLM-based applications requires a combination of technical measures, ethical considerations, ongoing monitoring, and user engagement to ensure trustworthy and dependable outputs.
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 certain something with the same prompt 10 times you might get 10 different outputs.
5. Prompt Injection Security Problem
When building an application based on LLMs, prompt injection is a potential issue. Users may force the LLMs to produce unexpected output. For instance, if you created an application to generate a YouTube script video based on a title, the user could instruct it to write a story instead.
Developing LLMs applications is enjoyable and can automate tasks while solving problems. However, challenges arise such as hallucinations, selecting the appropriate prompt context, ensuring output reliability and consistency, and addressing security concerns related to prompt injection.
References
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