Retrieval Augmented Generation

Retrieval Augmented Generation improves your LLM’s accuracy and makes its answers more reliable and relevant to your use case, customer base or brand voice.

What is RAG in AI?

Retrieval Augmented Generation (RAG) improves your LLM’s accuracy and makes its answers more reliable. By giving your AI model access to specific information through a database or other sources (called “grounding documents”), you can boost its responses so they’re always up to date and relevant to your use case, customer base or brand voice.

At Defined.ai, we’ve seen how powerful RAG AI can be when accuracy is a must but the model doesn’t need to know everything. Focusing the scope of the data ensures answers are highly relevant and correct, making it perfect for specialized topics. It’s also a more affordable option if you only need to add or update specific information for your model rather than retraining it completely. Speak to an expert

An example LLM fine-tuning workflow showing a specific information source being used to support an AI model's response.

Setting up training data for RAG, testing the outcome and adding annotations all take time. Let our specialists generate questions and answers from your grounding documents, provide cited answers and more so you can focus on your business!