What is a generative feedback loop and how does it help?
A generative feedback loop adds another dimension to RAG by making it a two-way street. It allows generative models to directly integrate the feedback as per the user's preferences.
Generative feedback loop's integration with LLMs can be broken down into the following four-stage process:
1️⃣ Data Collection: This is the initial stage where relevant data is gathered. In the context of LLMs, data can include textual content, user interactions, and feedback.
2️⃣ Generation: In this stage, the LLM generates outputs based on the collected data. This could involve creating text, answering questions, or producing other forms of content.
3️⃣ Feedback Collection: After the model generates an output, it is crucial to collect feedback on its performance. This feedback can come from users, automated systems, or domain experts. The feedback evaluates the generated content's accuracy, relevance, and quality.
4️⃣ Continuous Loop: The final stage involves updating with the new data to address any deficiencies. The goal is to enhance the model's performance over time, making it more accurate and reliable with each iteration.
These stages create a continuous loop where the model learns and improves iteratively.
If you're interested in diving deeper into these topics and exploring the future of AI, tune in to our podcast episode, The Future of AI: LLMs, AGI, and Beyond, featuring Bob van Luijt, Co-founder and CEO of Weaviate: • The Future of AI: LLMs, AGI, and Beyo...
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