Retrieval Augmented Generation (RAG) is a powerful technique that enhances the factuality of Large Language Model (LLM) outputs by incorporating information from external knowledge sources. However, even with RAG, LLMs can still hallucinate, generating incorrect or irrelevant content. One way to combat this is to have the LLM double-check its work by verifying its responses against the retrieved evidence. While effective, this method can be computationally expensive. A more efficient approach uses smaller, faster Natural Language Inference (NLI) models to perform this verification. But these lightweight models aren’t always as accurate, especially when dealing with the complex outputs of real-world RAG systems. The challenge lies in adapting NLI models to the specific domain of the RAG system’s knowledge base. Existing NLI models are often trained on simpler data, creating a mismatch with the real-world complexities of RAG. This performance gap becomes even more pronounced due to the lack of labeled examples in the target domain, which makes traditional supervised training difficult. Researchers have introduced a new method called Automatic Generative Domain Adaptation (Auto-GDA) to address this challenge. Auto-GDA focuses on generating synthetic training data that mimics the style and complexities of real-world RAG outputs. This synthetic data is then used to fine-tune a lightweight NLI model, effectively adapting it to the target domain without the need for manually labeled examples. Unlike previous methods that rely on manual filtering and augmentation strategies, Auto-GDA automates this process. It generates an initial set of synthetic data, then iteratively refines it using a “teacher” model (like a more powerful but slower LLM). This teacher model provides weak labels, indicating how likely the synthetic examples are to be true. Auto-GDA uses these weak labels to select the most promising samples for the next iteration, improving the quality of the synthetic data over time. The results are promising. Auto-GDA-trained NLI models have shown significant improvements in accuracy, often surpassing even the teacher models used to guide their training. Crucially, these fine-tuned models achieve near-LLM performance in grounding verification while being far more efficient. This opens the door to real-time fact-checking in industry applications. While Auto-GDA represents a significant step, challenges remain. One key area of future research is adapting this method to scenarios where the distribution of evidence samples isn't known beforehand. This would involve clustering and summarizing documents in a knowledge base to reflect real-world usage more effectively. Additionally, exploring multi-domain adaptation could further streamline the process, enabling efficient models to handle various RAG applications without compromising performance.