The content and data that feed into an AI system are just as important as the engine that drives it. This component determines what information the AI has access to and how it uses that information to generate responses.
Constrained Content for Better Performance Instead of overwhelming the AI with massive amounts of data, you can enhance its performance by carefully selecting and constraining the content it accesses. This approach is known as Retrieval Augmented Generation (RAG). By focusing on specific, high-quality content, you can achieve more accurate and contextually relevant responses.
The Power of RAG and RAGLITE RAG allows you to fine-tune the AI’s output by controlling the data it processes. There’s also a "lighter" version, RAGLITE, where you’re simply more selective about the content rather than programmatically controlling it. This method can be particularly effective for delivering high value with lower-cost LLMs.
Case Study: The Impact of Constrained Content In a recent project, we applied this approach by carefully constraining the input data. The result was an AI service that delivered highly accurate and context-aware responses, quickly and efficiently. This method allowed us to layer in not just content but also context, making the AI’s responses more human-like and nuanced.
Feeding your AI the right data and content is crucial for ensuring its responses are not just correct, but also relevant and useful.
