Get 10x efficient in building production-ready GenAI RAG applications

The text discusses the challenges of building production-ready applications using large language models (LLMs) like OpenAI's ChatGPT. It highlights that while LLMs are great for automating tasks, they have limitations due to their context window size and inability to interpret proprietary or real-time data. Retrieval Augmented Generation (RAG) pipelines can help by fetching relevant context related to user queries, but there is a need for larger context windows. The text also mentions the importance of dealing with various decisions like choosing the best vector DB, chunking strategy, and model in terms of cost and performance. It introduces Hasura and Portkey as tools that can boost productivity by enabling the creation of secure data APIs and adding production capabilities to RAG apps, respectively.

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