At Source Allies, we excel in integrating Generative AI and Large Language Models (LLMs) into our solutions to optimize error-prone and time-consuming manual processes. However, the real power of Generative AI lies in creating domain-specific solutions that truly differentiate your business.

Why Custom AI is Important

AI is often described as the rising tide that raises all ships in the harbor. As Reid Hoffman, Co-founder of LinkedIn, states, “It creates a wave of disruption.” Mustafa Suleyman, in The Coming Wave, advises, “Be the disruptors, not the disrupted.” The crucial question then becomes: if AI raises all ships, how will companies truly stand out and differentiate themselves? The answer lies in creating domain-specific LLM applications.

Generic, out-of-the-box Generative AI solutions, such as Salesforce Einstein, Office CoPilot, and Amazon Q, are designed to work generally across all domains. While these solutions often use a naive Retrieval Augmented Generation (RAG) pattern to provide up-to-date information, they are not optimized for specific domains or specific tasks. This limitation can hinder their effectiveness in addressing particular business needs.

At the end of the day, AI business value comes from solving particular problems. To build AI applications that add genuine value, it’s crucial to start with clear business goals and ensure everything—from model selection to evaluation and monitoring—aligns with these goals. An approach to drive measuring back to the business goal as the LLM app is created is known as Metric-Driven Development (MDD). MDD ensures that we measure success based on established criteria and continuously validate our progress.

We combine RAG with Metric-Driven Development and Task LLM Evaluations to tailor our Generative AI solutions to meet the unique needs of your business. Our expertise in LLM evaluations enables us to quantify and demonstrate the value of the LLM apps we build together. By aligning AI development with business goals and rigorously measuring results, we build task-specific, domain-specific AI that delivers measurable business value. AI in enterprises moves at the speed of trust and we know that quantifiable measures are a critical part of building that trust.

Proven Trust & Benefits

We have developed numerous Generative AI solutions, which have shown significant benefits across various industries. For example, we have developed an AI solution for a startup focused on generating comprehensive appeal letters to be sent to an individual’s health insurance company, drastically reducing the time needed to generate appeal letters to under two minutes and fully automating the workflow. In another instance, a crop insurance company governed by a massive corpus of regulation could make over 500,000 pages of documentation available for quick discovery. Outside of regulated industries, we have developed AI solutions that have enhanced the visibility of content traditionally hidden away, measurably improving accuracy and reducing the development cycle time by an order of magnitude.

We utilize a number of metrics when evaluating a Generative AI solution over time. Some of these metrics include:

• Answer Correctness
  • A weighted sum of both factual and semantic similarity, the accuracy of generated result to the ground truth - aka facts
• Faithfulness
  • Consistency of the generated result against the given document, how many claims in the result can be inferred from the given documents
• Document Relevancy
  • How relevant were the underlying document sources used to the question being asked

The combination of these metrics allows us to measure the performance of our AI solution accurately, make informed decisions on how to improve it, and provide direct insight into the application’s real-time usage. We delve deeper into the reasoning and process behind the choosing of these and various metrics for Generative AI Applications in our “Turning RAG to Riches: The Golden Metrics of Metrics-Driven Development” blog.

Building AI you can trust is a journey that involves continuous delivery and continuous improvement along with rigorous measurements. Here at Source Allies, we utilize industry best practices to deliver AI solutions that are not only effective but also reliable. By adopting these proven patterns, like Extreme Programming and Metric-Driven Development, businesses can incrementally integrate AI into their operations, allowing for scalable solutions tailored to the size of the problem, fostering both trust and the value of AI in real-world applications throughout every step of the process.