Learning from ChatGPT

#4142 Job Review Bullets and Planning #4142  A. Rewrite the following as bullet points for my annual review and planning: I have pro-actively and by assignment applied AI to my daily and ongoing S&MA work for NASA. I have helped and encourage others to learn AI. B. I plan to continue applying AI in my ongoing review of NASA program documents, automating review workflows, and creating review assistants Annual Review: AI Integration & Strategy Key Accomplishments Proactive AI Implementation: Successfully integrated generative AI into daily Safety & Mission Assurance (S&MA) workflows, both through self-directed innovation and assigned mission tasks. Organizational Upskilling: Actively mentored and encouraged colleagues in the adoption of AI, fostering a culture of continuous learning and technical literacy across the team. Future Objectives & Planning Document Intelligence: Expand the use of AI for the rigorous review of NASA program documents , ensuring higher...

#4141  PostgreSQL Database for RAG and KG #4141  Discuss  PostgreSQL  database for RAG PostgreSQL has evolved from a traditional relational database into a powerhouse for Retrieval-Augmented Generation (RAG) . While specialized vector databases (like Pinecone or Milvus) exist, many developers are sticking with Postgres because it allows them to keep their structured data and vector embeddings in the same place. Here is how Postgres handles the RAG workflow and why it’s becoming a top choice. 1. The Secret Sauce: pgvector Standard Postgres doesn't "speak" vectors out of the box. The pgvector extension is the industry standard that adds vector capabilities. It allows you to: Store embeddings (arrays of numbers representing text/images) in a VECTOR column. Perform similarity searches using distances like Cosine Distance , L2 Distance , or Inner Product . Query your vectors using standard SQL. Example Query SELECT content, metadata FROM documents ORDER BY embed...