There is still much to learn about large language models (LLMs) and the various types of hallucinations they can produce, with many teams having been working on LLM applications for over a year. Building production-ready apps using LLMs remains challenging due to their complexity, but a group of AI builders has compiled an in-depth guide to help streamline RAG workflows and optimize performance. The guide provides actionable tips such as reranking for relevance, efficient embedding, implementing query classification, selecting appropriate retrieval methods, and optimizing chunking. Additionally, multimodal models are gaining popularity, but they can also be prone to hallucinations, which can be mitigated by understanding the different types of hallucinations across modalities, detecting and evaluating extrinsic hallucinations, and learning how to evaluate generative AI initiatives effectively. The GenAI infra stack is constantly evolving, with efforts to codeify the various components powering the AI revolution.