RAG for Enterprise Solutions
The following text is my own take on this excellent interview where Arjun Prakash (CEO of Distyl AI) and Jerry Liu (CEO of LlamaIndex) discuss the hurdles and opportunities of Generative AI solutions for enterprise applications. I highly recommend watching it!
LLMs and Generative AI are great at following instructions and have become a major game-changer lately due to its efficiency in automating tasks and quickly extracting knowledge. However, the practical use of these tools in business operations is somewhat limited.
A common misconception is that creating applications with LLMs is easy – just throw some documents, connect to the LLM, and you are done. A small parenthesis here, this seems to be true for the vast majority of AI application around. In reality, it’s much more complex. Many people are working on developing applications using LLMs, especially for prototypes, but face challenges when moving to a production environment. So, what’s the issue?
Integration of systems: Integrating LLMs with other systems introduces complexity and potential errors, particularly related to the addition of more parameters. Considerations extend to incorporating retrieval systems, involving various processes such as loading, ingesting, parsing, placing data into a vector database, embedding it, and determining retrieval methods.
Complete SaaS solution: It is necessary to offer a complete solution that can address growing needs and user loads, enhance process automation, facilitate seamless connectivity with other tools and systems, and incorporate robust security measures.
Stochastic Nature of LLMs: The LLM itself acts like a bit of a black box. Different techniques and tools are needed to ensure it works as intended. These often require a customized approach, working backward from the use case to find the best combination of techniques. While using LLMs correctly can create substantial value, thinking of them as plug-and-play solutions might impress in a demo but can lead to disappointment in a real production setting.
Enterprise solutions at scale with RAG
Unlocking the true value of AI in business happens when seamlessly integrating it into daily operations. It’s crucial to find the best ways to use this new technology, and for this a good idea is to start from the problem you want to solve and develop a solution backwards. When integrated into the heart of a business, generative AI adds significant value by streamlining operations, improving decision-making, and shifting decision making from acting reactively to acting proactively.
Using RAG to enhance the supply chain’s efficiency can help illustrate this. An implemented solution would aid in making improved decisions and standardizing choices for optimal inventory planning across the workforce. Through a strategic use of generative AI, traditional decision-making approaches can be substituted with standardized information retrieval, resulting in higher productivity, consistency, and efficiency that encourages proactive engagement and enhances overall operational performance.
Nevertheless, a key concern is the reliability of these solutions. To enhance and consistently improve it in the short term, specific crucial measures must be taken. Here are essential points to ensure the reliability and continuous improvement of a RAG system:
Innovate and Embrace Unsuccessful Ideas: Persist in experimenting with innovative ideas, understanding that integrating this new technology involves a learning process with mistakes. This requires skill in handling data and selecting components that fit specific use cases and user requirements.
Quick Iterations: It’s not enough to make mistakes and try new ideas; it’s crucial to iterate from these experiences quickly. Applying established best practices in software engineering reliability is vital for maintaining a high iteration speed.
Data-Driven Evaluation: Despite the pre-training of LLMs, having evaluation data is essential. This ensures continuous assessment and improvement of the system. Remember, you need to evaluate both the LLM and your retrieval system.
Customization in Metrics: While standardized metrics are valuable, the importance of customization cannot be overstated to ensure alignment with the unique aspects of a specific use case. Specifically, in the creation of RAG, evaluating the retrieval system is a fundamental metric, separate and distinct from the LLM itself.
Constant Feedback: Gathering feedback is crucial for meaningful iterations. This includes user input, identifying recurring issues, insights into retrieval and model performance, latency, and contributions to enhancing adaptive routing techniques.
Currently, we are working to understand the existing design patterns and evaluate the potential impacts of new solutions. Our goal is to carry out effective experimentation efforts that not only work well but also contribute to increased adoption rates. To achieve this, adaptability and dynamism are crucial. It is essential for us to understand the operational challenges inherent in a process, allowing us to build solutions from the ground up with a comprehensive understanding of the complexities involved.