📅 2025-05-11 — Session: Developed frameworks for financial and AI systems analysis
🕒 17:20–18:05
🏷️ Labels: Financial Analysis, Ai Systems, Metadata Management, Decision Making, Communication Strategies
📂 Project: Business
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore and develop frameworks for both financial decision-making and AI memory systems.
Key Activities
- Business Communication and Decision Analysis: Discussed strategies for framing business proposals and created a ‘loss leaderboard’ for decision analysis. Explored frameworks for managing costs associated with poor quality, including COPQ and Lean Six Sigma methodologies.
- AI Memory Systems: Provided a technical overview of memory-augmented transformer architecture and analyzed memory and retrieval techniques in AI systems. Conducted a deep dive into user memory object encoding and vectorstore mastery.
- Data Management: Executed scripts for extracting and normalizing metadata from vectorstore into Pandas DataFrame, focusing on Chroma and FAISS with LangChain.
Achievements
- Developed a structured approach to financial decision analysis and communication strategies.
- Enhanced understanding of AI memory systems and their technical frameworks.
- Successfully extracted and normalized metadata into a Pandas DataFrame.
Pending Tasks
- Further exploration of advanced techniques in vector databases and embedded memory systems.
- Implementation of discussed frameworks in real-world scenarios to assess effectiveness.