David Gloyn-Cox
he/him
David Gloyn-Cox

David Gloyn-Cox

Python

2 stories

David Gloyn-Cox

David Gloyn-Cox

knowledge

3 stories

Beginners visualization of graphed entites
David Gloyn-Cox

David Gloyn-Cox

ds

19 stories

An overview of the RAG pipeline. For documents storage: input documents -> text chunks -> encoder model -> vector database. For LLM prompting: User question -> encoder model -> vector database -> top-k relevant chunks -> generator LLM model. The LLM then answers the question with the retrieved context.
David Gloyn-Cox

David Gloyn-Cox

data

5 stories

A flow chart of sticky notes in Miro
David Gloyn-Cox

David Gloyn-Cox

strategy

5 stories

David Gloyn-Cox

David Gloyn-Cox

modelling

3 stories

David Gloyn-Cox

David Gloyn-Cox

api

3 stories

David Gloyn-Cox

David Gloyn-Cox

dev

4 stories

Twitter screenshot sample
David Gloyn-Cox

David Gloyn-Cox

RAI

1 story

David Gloyn-Cox

David Gloyn-Cox

React

3 stories

David Gloyn-Cox

David Gloyn-Cox

apple

1 story

Python in iPad
David Gloyn-Cox

David Gloyn-Cox

neo4j

3 stories

David Gloyn-Cox

David Gloyn-Cox

viz

1 story

David Gloyn-Cox

David Gloyn-Cox

he/him

~ Developer by quia ~ Problem solver by nature ~ Book lover by desire ~ Curiosity by chance ~ Enterprise Architect by vocation ~ he/him ~