Generative Feedback Loops with LLMs for Vector Databases

Generative Feedback Loops is a concept where we use data from the database to supplement the factual knowledge of generative models and then write the generated outputs back to the database for future use. This technique can be used in various applications such as generating advertisements, summarizing podcasts, categorizing tweets, and even building autonomous AI assistants like AutoGPT. By saving intermediate results, we can create a feedback loop that improves the performance of generative models over time.