The rise of large language models (LLM) has transformed the field of Artificial Intelligence over the past year. These technologies enable businesses and developers to create smarter applications with advanced linguistic capabilities. However, successfully implementing an LLM-based solution remains a challenge, not only from a technical perspective but also from a business standpoint.
In this article, we will address some of the key points that we at Daus Data have identified as fundamental for successfully deploying an LLM-based solution into production.
Balanced Use Case
Selecting the appropriate use case is crucial for successfully implementing an LLM-based solution. It is key to choose a use case that provides tangible value and solves specific problems, justifying the investment and ensuring the adoption of the solution.
The focus should not be on finding a grand use case that solves the most complex problem, but on finding a balance that allows for impactful results without a significant initial investment. At Daus, we believe the ideal starting point is an internally-oriented use case, such as a document assistant. This would enable employees to quickly access the company’s internal documentation, reducing search times and support tickets.
Adaptability and Evaluation
Once the use case is identified, the next challenge is to select and adapt the LLM model to the specific needs of our project. There are multiple variables to consider to successfully develop the solution, which involves using different technologies and requires a considerable learning curve, making this step challenging.
To overcome these challenges, we at Mática Partners Group have developed a framework that helps us, on one hand, to automatically evaluate the various models available and choose the most suitable one. On the other hand, it simplifies interoperability between different technologies, reducing the learning curve and development time.
Thus, the Mática Partners Group framework hides much of the inherent difficulties in this process, facilitating the adaptation of the LLM model to the specific needs of our use case.
Explainability
Once the model is developed, we need to provide it with explainability to ensure trust in the results obtained. Our colleagues at Mática Partners recommend that the developed models always provide references to the information they are based on to give users confidence in the displayed results.
Additionally, from a development standpoint, companies like Anthropic are equipping these models with tools that allow tracing how the model reaches the obtained result, step by step. This way, total confidence in the system’s provided results is achieved.
Explainability and the ability to trace the model’s reasoning process are key aspects to generate trust among the end users of the LLM-based solution.
Security
Use cases involving LLM models often require handling large amounts of data, which frequently includes the handling of sensitive information. Therefore, it is crucial that our developments have high levels of security, ensuring that information is properly isolated and only available to authorized individuals.
Moreover, it is essential that models do not respond to unethical questions or inappropriate language to avoid undesirable situations.
To address these challenges, the Mática Group has developed, within the framework, a security module that provides protection for our developments. This module manages access to the model and controls what the system can respond to, based on the user utilizing it, preventing unauthorized access and ensuring information security. Additionally, it protects the model from using inappropriate language and/or responding to unethical questions.
Integration with AWS
To carry out the development, it is essential that our model can interact with various AWS services to query the necessary information.
Ensuring all these elements work harmoniously and integrating them requires significant effort. To address this challenge, the Mática Partners Group has included an interoperability module in our framework that already incorporates various connectors for these AWS services.
Our framework integrates with the Opensearch service, which, with its vector database, allows us to provide context to the model for document information search. Additionally, it integrates with Athena, RDS, and Redshift to query analytical information from our Data Lake as well as relational databases.
Thanks to this module, the integration effort is drastically reduced, ensuring our use case is 100% integrated into the AWS platform. This way, the deployment and operation process of the LLM-based solution is significantly simplified.
In summary, successfully implementing an LLM-based solution requires considering various key aspects. Thanks to the Gen-AI Framework developed by the Mática Partners Group, these processes can be drastically simplified, reducing the learning curve and accelerating development.
Thus, businesses and developers can leverage the full potential of LLMs to create smarter applications with advanced linguistic capabilities without facing major technical and business challenges.