GenAI app developers must cope with new RAG complexity

Retrieval-Augmented Generation (RAG) is quickly becoming a necessary element of generative AI applications. RAG gives pre-trained AI models superpowers of specialization, making them precise and accurate for vertical or task-specific applications. However, RAG also introduces new traffic, security, and performance requirements to your GenAI stack. With RAG comes new complexity and challenges that organizations must address with a more sophisticated AI infrastructure.

A short RAG introduction

RAG works by augmenting AI inference with relevant information from external data stores that are not included in the base model’s training corpus. This method provides domain-specific knowledge to the AI ​​model without the need to retrain the general model. In general, RAG models produce answers that are more contextually rich, accurate, and factually consistent. RAG can even be used to improve the performance of open-domain AI applications. RAG also makes AI inference more efficient by reducing the need for in-model data storage. This has several positive spillover effects.

RAG models can be smaller and more efficient because they do not need to encode all possible information in their parameters. Instead, they can dynamically retrieve information as needed. This can result in smaller memory requirements and lower computational costs because the model does not need to store and process a large amount of information internally.

• Lower training costs: While the retrieval mechanism is primarily used during inference, the ability to train smaller models based on external data sources can reduce overall training costs. Smaller models typically require less computational power and time to train, resulting in cost savings.
• Scalability: RAG architectures can scale more effectively by distributing the load between the generative model and the query system. This separation enables better resource allocation and optimization and reduces the overall computational load of individual components.
• Easier updates: Because RAG uses an external knowledge base that can be easily updated, the entire model does not need to be frequently retrained to incorporate new information. This reduces the need for continuous, expensive retraining processes and allows the model’s knowledge to be updated in a cost-effective manner.
• Real-time relevance: Due to the time it takes to train models, many types of data become stale relatively quickly. By retrieving data in real time, RAG ensures that the information used to generate it is always up-to-date. This also makes GenAI apps better suited to real-time tasks, such as turn-by-turn navigation in a car or weather reports, to name just two examples.

The benefits of RAG are clear, but adding what is effectively a new query, routing and traffic management layer introduces additional complexity and security challenges.

Traffic control

One of the biggest challenges with RAG is the increasing complexity of managing traffic. RAG architectures rely on retrieving relevant documents or information in real-time. This can result in a significant increase in traffic, which can cause bottlenecks if not managed properly. This also means that application performance depends not only on end-user latency and responsiveness, but also on information quality. If RAG is slow, GenAI may still respond, but with lower quality results.

Security and compliance concerns

Security is another important issue when integrating RAG into GenAI applications. Retrieval often requires access to proprietary databases or knowledge bases, increasing the potential attack surface. Ensuring the integrity and security of these data sources is critical to prevent data leaks or unauthorized access. RAG can also introduce new compliance issues if the data retrieved falls under regulations, such as those that apply to the financial or healthcare industries. Often the RAG layer is the logical place for this data, but that also means that the RAG database must meet any necessary regulations (HIPAA, Gramm-Leach Bliley, SOC2, etc.).

Teams should employ robust authentication and authorization mechanisms to secure their RAG infrastructure and data retrieval process. This also means that robust API security must be put in place for any service – internal or external – that accesses a RAG stack. Encrypting RAG data in transit and at rest can protect sensitive information. Since much of the sensitive data is stored in RAG, it is also well suited to stricter authentication policies and zero-trust deployments.

Data quality and relevance

The effectiveness of a RAG system depends heavily on the quality of the data retrieved. Poor quality or irrelevant data can lead to inaccurate or nonsensical results from the generative model. In real-time applications, the timeliness of the data is also crucial. If the RAG system pulls data from third-party data sources, the GenAI application is subject to data quality risks in the supply chain. In enterprise applications or applications in sensitive areas such as medicine or law, the tolerance for poor responses due to poor data quality is close to zero.

To overcome this, teams should invest in maintaining high-quality and up-to-date data sources and build automated data pipelines with redundant quality checks. They should also continuously monitor user behavior and feedback for signs of data quality issues. Continuous monitoring and evaluation of system output can also provide insights into areas that need improvement.

Don’t let yourself get upset

If you are deploying GenAI applications, you probably already have RAG now or will in the future. The benefits are tremendous. However, successful RAG rollouts require planning and consideration. While RAG offers significant benefits by improving the specialization and accuracy of generative AI applications, it also brings with it a number of complex challenges. Effective traffic management, strict security measures, performance optimization, ensuring data quality, and handling integration complexity are essential to successfully implementing RAG in GenAI stacks. For application delivery teams struggling with GenAI challenges, RAG is a powerful way to make almost everything in their AI apps run better—with the right preparation and mindset.

Brett Wolmerans is a senior product manager at F5 NGINX. Having spent years working in the industry as a practitioner and engineer, Brett knows that technology never stands still. Brett is always finding new ways to clearly communicate the latest research and development results…

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