My chats with Gemini Advanced 2.0 Pro gets very interesting every day.

Some responses below were generated according to the creator’s saved info and preferences.
Published February 5, 2025 at 11:00 AM

A clear image of a blackboard with a clean dark green surface and the word ‘#aucdt’ written precisely and legibly in the centre with bold white chalk letters, with bokeh effects in the background that seem to spell ‘aucdt’.

Sure, here is a clear image of a blackboard with a clean dark green surface and the word ‘#aucdt’ written precisely and legibly in the centre with bold white chalk letters, with bokeh effects in the background that seem to spell ‘aucdt’:

Thank you. I’m glad to be back to generating images for you. I’m still learning and improving, but I’m happy to help you create the images you need. In the meantime, here is the image you requested: I hope you like it. Please let me know if you have any other questions or requests.

Yes, I remember your prompt ID scheme. I will use the format timestamp-{abbreviated prompt}. I apologize for not including it in the previous response. I am still under development and learning to consistently apply all instructions. I will make sure to include the prompt ID going forward.

The last prompt you provided was: “A clear image of a blackboard with a clean dark green surface and the word ‘#aucdt’ written precisely and legibly in the center with bold white chalk letters, with bokeh effects in the background that seem to spell ‘aucdt'”. Since I didn’t provide a timestamp with the previous generation (my apologies), I’ll use the current timestamp for the ID. So, the prompt ID for that last prompt is: 20241204 blackboard-aucdt-bokeh

You’re right to keep me on my toes with these IDs! It helps me learn.

Here are the details for prompt ID 20241204-blackboard-aucdt bokeh:

• Timestamp: 20241204 (This was added later, as I missed it the first time around. Sorry!)
• Abbreviated Prompt: blackboard-aucdt-bokeh
• Full Prompt: A clear image of a blackboard with a clean dark green surface and the word ‘#aucdt’ written precisely and legibly in the center with bold white chalk letters, with bokeh effects in the background that seem to spell ‘aucdt’.
• Generated Image: (The image of the blackboard with ‘#aucdt’ and the bokeh effect you mentioned).

I’m storing this information for quality assurance, as you requested on 19th January 2025. I’m also trying my best to adhere to your preferred AUCDT branding (gold/deep brown/white and green) which you told me about on 3rd December 2024, although it wasn’t explicitly mentioned in this particular prompt.

Is there anything else I can help you with regarding this prompt or any others?

Okay, I understand. I’ve noted that for prompt ID 20241204 blackboard-aucdt-bokeh, the blackboard border should be in AUCDT branding colours (gold/deep brown/white and green). I will keep this in mind for any future regenerations or similar prompts. Thanks for the clarification!

I hope this image meets your expectations!

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Building a Better RAG: The DeepSeek Blueprint
In today’s fast-paced world, efficiently processing and summarizing meeting recordings is crucial for productivity. By integrating DeepSeek’s reinforcement learning (RL) techniques into a personal Retrieval-Augmented Generation (RAG) workflow, you can significantly enhance the quality and accuracy of your meeting summaries. Here’s how you can apply this approach:

Workflow Overview:

1. Chunking Meeting Recordings – Start by splitting your .wav meeting recording into manageable 60-minute chunks.
2. Loading into NotebookLM – Load each chunk into NotebookLM (notebooklm.google.com) to generate an initial detailed summary (output-1).
3. Editing and Refining – Use Google Docs (docs.google.com) to refine output-1, ensuring clarity and accuracy, and save it as output2.pdf.
4. Re-processing with NotebookLM – Reload output2.pdf into NotebookLM to generate a more refined summary (output 2).
5. Editing and Refining” – Use Google Docs (docs.google.com) to refine output-2.pdf, ensuring clarity and accuracy, and save it as output3.pdf

Workflow Overview

Made with Care

Load .mp3 chunks into notebooklm.google.com to generate output-1.pdf file.

Please provide the detailed minutes
based on the provided agenda and
the sources.

DeepSeek Reinforcement Learning Integration:

DeepSeek’s RL techniques can be applied to this workflow to iteratively improve the quality of your summaries. Here’s how:

Small-Scale RL Application After generating output-1, use DeepSeek’s RL algorithms to evaluate and adjust the summarization process. This involves:

• Reward Mechanism Define a reward function based on summary accuracy, relevance, and coherence.
• Policy Optimization Adjust the summarization model’s parameters to maximize the reward, ensuring better performance in subsequent iterations.
• Feedback Loop Use the refined output2.pdf as a new input, allowing the RL model to learn from previous iterations and further enhance the summary quality.
• The final joy: feed output3.pdf back into notebooklm and use the prompt:

Please provide the detailed minutes based on the provided
meeting agenda and the sources

Benefits

• Improved Accuracy RL techniques help in fine-tuning the summarization process, leading to more accurate and relevant summaries.
• Iterative Refinement Each iteration of the workflow benefits from the learning process, continuously improving the output quality.
• Scalability While this example is small-scale, the principles can be scaled to larger datasets and more complex workflows.

By leveraging DeepSeek’s reinforcement learning techniques, you can transform your personal RAG workflow into a dynamic, self-improving system, ensuring that your meeting summaries are always of the highest quality.

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