Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) have rapidly gained prominence in the banking and insurance industries. Many companies use Retrieval Augmented Generation (RAG) solutions for their knowledge management or create chatbots for customers. But how can companies use these technologies safely and productively? How can system failures be avoided? To operate critical AI systems with low fault tolerance, additional components are needed alongside LLMs.
The limits of LLMs
What fault tolerance and hallucinations are all about
LLMs excel in their ability to understand and generate human language.
But there are also disadvantages: they sometimes tend to produce so-called hallucinations, i.e., they may invent facts, provide inaccurate information or the results contradict the prompt. For example, a user wants to find out the medicinal benefits of a particular plant and the model lists several positive effects. However, these effects have not been proven or may even pose a health risk. In critical areas of application, this can have serious consequences, as customers could receive false information or an incorrect basis for decision-making leading to unfavorable decisions.
Transparency and explainability
LLMs often act as “black boxes.” It can be difficult to understand how they arrive at certain answers. This lack of transparency affects compliance and is an obstacle during audits, especially in regulated industries such as finance and insurance.
Contextual limitations
Up-to-date or company-specific knowledge is very important. But without additional data integration, LLMs can run into problems using it. This is because the models are based on static training data and are therefore not always up to date, which can lead to outdated or inexact answers.
Data protection concerns
What about sensitive data? Handling this data is critical. LLMs could unintentionally disclose confidential information or violate data protection guidelines. This poses a very significant risk for companies that are subject to strict regulatory requirements.
What are compound AI systems?
Advantages of compound AI systems
- Improved error control: By integrating validation and verification components, errors can be significantly reduced, producing higher quality outputs.
- Increased transparency and traceability: It enables the possibility to analyze intermediate results and trace decisions, which facilitates compliance.
- Adaptability: Integrating domain-specific knowledge and up-to-date data sources? Absolutely. This increases the relevance and timeliness of the results.
- Data protection and compliance: Companies benefit from the targeted data access offered by Compound AI systems, as it helps them comply with data protection guidelines.
Why component-based thinking is useful
Architecture and process design:
Breaking down complex problems into specialized subtasks makes it possible to develop and maintain them efficiently. Clear interfaces between components facilitate integration and promote the reusability of modules.
For example: Retrieval Augmented Generation (RAG)
RAG is an early example of an AI component system. It combines LLMs with external data sources to provide up-to-date and relevant information. This results in more precise output and reduces the likelihood of hallucinations.
Application example: How chatbots are improved by compound AI systems
Initial situation
An insurance company uses a simple chatbot based solely on an LLM. This often provides inaccurate answers and is unable to process customer queries in a personalized way.
Step-by-step optimization is the solution to this situation:
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- 1st step − It’s necessary to integrate a vector database: by connecting a database with company-specific information, the chatbot can provide accurate and personalized answers
- 2nd step − An error control component is critical: automated filters check the LLM’s output for consistency and accuracy, reducing the error rate.
- 3rd step – You need to integrate human expertise (human-in-the-loop): specialists validate critical responses and provide feedback that is used to continuously improve the system.
This leads to better results because:
- Quality of responses increases: Customers receive accurate and relevant information.
- Customers are more satisfied: Reliable answers increase trust in the chatbot and the company.
- Risks are minimized: Potentially incorrect answers are detected and corrected at an early stage.
What are the challenges and solutions implementing Compound AI Systems?
Mapping out the use case
It is essential to break down the use case into clear subtasks and select suitable components for each one, including data acquisition, processing and validation.
Complexity and interoperability
The integration of multiple components increases system complexity. Here, the use of standardized protocols and interfaces can efficiently organize communication between modules.
MLOps and operation
Customized MLOps strategies are necessary to ensure the operation and maintenance of modular systems. Continuous monitoring ensures that each module is functioning optimally.
What special requirements exist in the banking and insurance industries?
Data protection and regulatory requirements
Insurance companies in particular must comply with strict data protection laws such as the EU DORA regulation. Compound AI systems enable targeted control of the data flow, thus supporting compliance.
Trust and reliability
Customers expect correct and trustworthy information. Additional validation mechanisms in compound AI systems reduce errors to a minimum, which also strengthens customer trust.
Future-proof
Technology is developing rapidly. Thanks to their flexibility, AI component systems make it easy to integrate emerging technologies, thus providing long-term investment security.
Conclusion
To use LLMs and GenAI productively and securely, Compound AI Systems are essential. They address the limitations of LLMs and enable the development of secure, efficient, and future-proof AI applications. Companies that adopt this modular approach early on will gain a decisive competitive advantage.
Optimize your AI applications now by using AI component systems and stay one step ahead of the competition.
Let's get talking
Thomas Löchte
Informationsfabrik PART OF X1F
Dr. Leschek Homann
Informationsfabrik PART OF X1F
