Chatbots are becoming increasingly popular for interacting with users, providing information, entertainment, and assistance. However, building chatbots that can handle diverse and complex user queries is still a challenging task. One of the main difficulties is finding relevant and reliable information from large and noisy data sources. In this talk, I will present some of the latest advances in retrieval-augmented generation(RAG) techniques, which combine the strengths of both retrieval-based and generative approaches for chatbot development. Retrieval-based methods can leverage existing text documents to provide informative and coherent responses, while generative methods can produce novel and engaging conversations personalized to the user. I will cover the following topics: 1. Hybrid search with vector databases: How to use both keyword-based and semantic-based search methods to retrieve relevant documents from large-scale vector databases. 2. Query generation using LLMs: How to use large language models to generate natural and effective queries for document retrieval, based on the user input and the dialogue history. 3. Automatically excluding irrelevant search results: How to use various filtering and ranking techniques based on vector distance to exclude irrelevant search results. 4. Re-ranking: How to dynamically re-rank retrieved documents to further improve context relevance. 5. Chunking Techniques: How to use text segmentation and summarization methods to chunk long documents into shorter and more relevant passages. I will demonstrate the effectiveness of these advanced techniques in the RAG workflow. I will also discuss the challenges and limitations of these techniques and the future directions for research and development.