Retrieval-Augmented Generation (RAG) is a technique that enhances AI model performance by injecting up-to-date and domain-specific data from external sources into prompts before they reach a Large Language Model (LLM). This method helps address limitations such as hallucination and lack of transparency in LLMs by dynamically fetching relevant information without needing continuous fine-tuning. RAG consists of two main processes: the Ingest Pipeline, where documents are converted into vectors and stored in a vector database, and the Retrieve Pipeline, which fetches relevant data in response to user queries using techniques like Cosine Similarity. Kong's AI Gateway offers tools like the AI Prompt Compressor to optimize and compress prompts, reducing latency and cost, while the AI Prompt Decorator ensures that LLMs rely solely on vetted internal sources. Kong is also developing features to enhance the control and relevance of RAG-based responses, such as chunk relevance scoring and policy enforcement mechanisms, illustrating its commitment to advancing AI capabilities for various teams.