Retrieval-augmented generation (RAG) can enhance the reliability and personalization of outputs from language models (LLMs), but its effective implementation requires overcoming several challenges. These include building and maintaining integrations with third-party data sources, ensuring fast retrieval operations, configuring outputs to include source information accurately, and handling sensitive data in compliance with privacy regulations. Additionally, the use of unreliable data sources can lead to inaccuracies in LLM outputs. Merge, a unified API solution, addresses these challenges by providing easy access to comprehensive and accurate data through numerous integrations, ensuring the LLMs receive well-structured and reliable data to generate high-quality outputs consistently. The platform aids companies like Guru and Causal in powering AI features effectively by managing integrations and normalizing data to mitigate the unpredictability of LLMs.