🔧Building a chatbot requires retrieval augmented generation.
🧠Language models (LMS) rely solely on training data for knowledge.
🔎Retrieval augmented generation helps LMS access external knowledge sources.
💡Source knowledge in prompts enhances the performance of LMS.
💭Contextual information can be added to prompts for better understanding.
What is retrieval augmented generation?
—Retrieval augmented generation combines retrieval-based systems and language models for better performance in understanding and answering questions.
Why do language models (LMS) have hallucinations?
—LMS rely solely on the knowledge they learn during training and may lack knowledge about specific topics or recent events.
How does retrieval augmented generation enhance chatbot performance?
—Retrieval augmented generation allows chatbots to access external knowledge sources, improving their ability to provide accurate and relevant information.
What is source knowledge in prompts?
—Source knowledge refers to any external information added to prompts that helps enhance the performance of language models.
How can contextual information be added to prompts?
—Contextual information can be inserted into prompts, providing additional context and aiding the language model in better understanding and generating responses.
00:00Learn how to build a chatbot using retrieval augmented generation
02:29Retrieval augmented generation combines retrieval-based systems and language models
02:51Language models rely solely on the knowledge they learn during training
06:02Retrieval augmented generation allows chatbots to access external knowledge sources
08:41Source knowledge in prompts enhances the performance of language models
12:32Contextual information can be added to prompts for better understanding
