Why An AI App Builder Should Not Use LLM Only

Introduction

Building AI applications exclusively with Large Language Models (LLMs) introduces significant risks and limitations that can undermine the success and reliability of enterprise applications. While LLMs offer remarkable capabilities for natural language processing and code generation, relying solely on them creates several critical vulnerabilities that modern AI app builders must address.

Limited Customization and Flexibility

LLM AI app builders struggle with customization when complex, highly tailored requirements emerge. While these platforms excel at generating standard applications quickly, they frequently fall short when unique functionalities are needed. The drag-and-drop interfaces and pre-built modules that make LLM-based tools accessible become constraints when businesses require specific domain features.

For businesses with specific domain requirements, this limitation can necessitate costly transitions to traditional coding approaches. The rigid nature of LLM-only solutions means developers often cannot implement the precise functionality needed for enterprise-grade applications.

Context and Architectural Understanding Deficiencies

LLM AI app builders struggle with contextual understanding, which is crucial for enterprise-grade applications. Research shows that 65% of developers report AI missing context during refactoring, and approximately 60% experience similar issues during test generation and code review. These tools often lack the ability to comprehend broader system architecture, leading to code that may be syntactically correct but fails to align with existing codebases or follow established patterns.

LLMs process input within a fixed token window (e.g., 4,000 – 8,000 tokens for many models), meaning they “forget” information beyond that range. For example, in a multi-turn conversation about troubleshooting a software bug, the model might lose track of earlier steps or user-provided code snippets, leading to repetitive or irrelevant suggestions.

Hallucination and Reliability Issues

AI hallucinations occur when an LLM generates output that sounds confident and fluent, but is factually inaccurate, made-up, or misleading. LLMs generate text based on statistical likelihood, not truth. For instance, when asked for historical dates or technical specifications, they might confidently produce incorrect information.

Recent studies indicate that over 30% of AI-generated code contains security vulnerabilities, including command injection, insecure deserialization, and unsafe API usage. Additionally, repeated AI iterations can actually increase vulnerability rates by 37.6%. Common issues include:

• Misinterpretation of requirements leading to functionally incorrect solutions

• Syntax errors and incomplete code generation

• Missing edge cases and inadequate error handling

• Hallucinated objects referencing non-existent libraries or methods

Security and Compliance Risks

AI-generated code is only accurate 65% of the time, with some tools producing code that is correct just 31% of the time. This leaves organizations open to exploits, bugs, and compliance risks. The foremost security risk of AI-generated code is that coding assistants have been trained on codebases in the public domain, many of which contain vulnerable code.

At least 48% of AI-generated code suggestions contained vulnerabilities. AI apps introduce new attack surfaces including:

• Prompt injection: where users manipulate input to bypass intended behavior

• Model extraction: where attackers try to steal your model by hitting your API repeatedly

• Inference attacks: where private training data can be inferred from model outputs

Scalability and Performance Limitations

AI systems are designed to process vast amounts of data, perform complex tasks, and deliver real-time insights. However, scalability issues can hinder their performance and limit their potential7. High computational demand can lead to bottlenecks and performance degradation when scaling AI systems7.

LLM inference costs can spiral out of control if not managed effectively. For the 70 billion parameter model, GPT-4o calculations predicted a cost of about $12.19/user/month. Enterprise inference costs can range from $1K-50K on the low usage end to $1M-56M a year for high usage.

The Need for Hybrid Architectures

Hybrid AI represents a structured, comprehensive, and integrated application of both symbolic and non-symbolic AI. By combining rule-based and machine learning methods, it capitalizes on the strengths of both domains. The rule-based component ensures speed and reliability, while the machine learning component offers flexibility and adaptability.

AI-powered microservices have demonstrated remarkable system reliability, response times, and cost-efficiency advancements. Organizations leveraging AI-enhanced microservices experience a 47% reduction in deployment cycles and a 56% improvement in system reliability.

Production Monitoring and Maintenance Requirements

Model drift occurs when the performance of a machine learning model degrades over time due to changes in the underlying data. Without proper monitoring, even the most promising AI initiatives risk becoming expensive dead ends, unable to adapt to rising data volumes, increasing system complexity, or evolving business needs.

AI models fail in production due to various factors including:

• Data drift: When input data changes significantly from training data

• Concept drift: When the relationship between input features and target variables changes

• Covariate shift: When input feature distribution changes

Conclusion

While LLMs are powerful tools for AI application development, relying exclusively on them creates significant risks including limited customization, context understanding deficiencies, hallucination issues, security vulnerabilities, scalability challenges, and maintenance complexities. Successful AI app builders should adopt hybrid architectures that combine LLMs with traditional software engineering practices, proper monitoring systems, and comprehensive testing frameworks to build reliable, scalable, and secure enterprise applications.

The key is not to avoid LLMs entirely, but to use them as one component within a broader, well-architected system that includes proper validation, monitoring, security measures, and traditional software engineering practices to ensure long-term success and reliability.

References: