Part 7/8:
This approach isn’t limited to marketing—it's applicable to any domain needing conversational agents with memory. For example, you can customize prompts for customer support, sales, or other niches.
Additionally, Shapiro hints at possible improvements:
Using more sophisticated indexing for larger datasets.
Incorporating fine-tuning for specific datasets.
Extending memory length dynamically based on conversation context.
Conclusion
This in-depth tutorial showcases a modular method to give GPT-3 chatbots a semblance of long-term memory through embedding-based retrieval. By saving dialogue logs, embedding conversations, and searching for relevant past inputs, you can significantly enhance chatbot relevance and user experience.