This is a detailed guide on building a Retrieval Augmented Generation (RAG) pipeline using the Redis Vector Library. The author, Rini, shares their hands-on experience and journey into tech, from starting as a backend software engineer to becoming a Product Marketing Manager for AI at Redis. They built an AI assistant that can answer queries about a recent Nike earnings call, pulling relevant context from the earnings report and generating accurate responses using OpenAI's GPT model. The guide covers setting up the basics, including installing Python dependencies, configuring Redis, preparing data, defining the schema, loading data into Redis, querying the database, building the RAG pipeline, integrating OpenAI's GPT model, and testing the pipeline. Throughout the project, Rini encountered some challenges, such as dealing with an OpenAI API rate limit, but was able to overcome them using different solutions. The guide highlights the importance of effective data preprocessing, developer mindset when working with tools like Redis, and the potential of Redis and RAG pipelines in delivering precise, context-aware answers.