Supabase hired me to build ClippyGPT - their next generation doc search. We can ask our old friend Clippy anything you want about Supabase, and it will answer it using natural language. Powered by OpenAI + prompt engineering. In this video I will be showing you exactly how I did this, and how you can do the same in your projects. We'll be covering: - Prompt engineering and best practices - Working with a custom knowledge base via context injection + OpenAI embeddings - How to store embeddings in Postgres using pgvector Supabase blog post: https://supabase.com/blog/chatgpt-supabase-docs pgvector extension: https://github.com/pgvector/pgvector Generate embeddings implementation: https://github.com/supabase/supabase/blob/54d39d4958575e5b58aa1d5d2a02db863ab4673c/apps/docs/scripts/generate-embeddings.ts Clippy edge function implementation: https://github.com/supabase/supabase/blob/54d39d4958575e5b58aa1d5d2a02db863ab4673c/supabase/functions/clippy-search/index.ts Clippy frontend implementation: https://github.com/supabase/supabase/blob/54d39d4958575e5b58aa1d5d2a02db863ab4673c/packages/ui/src/components/Command/AiCommand.tsx Prompt engineering: https://prmpts.ai/blog/what-is-prompt-engineering 00:00 Why? 01:40 Let's get started 03:15 Custom knowledge base 04:49 Context injection 06:13 Pre-process MDX files 13:40 Embeddings 15:40 Storing in Postgres + pgvector 22:21 API endpoint (edge function) 23:44 Calculating similarity in pgvector 27:55 Prompt engineering 33:15 Prompt best practices 38:37 Demo time! 41:32 Thanks for watching!