What is the best practice for setting up your data to make sure it’s embedded properly for RAG vector so that the AI agent can really retrieve it correctly and accurately? I see a lot of YouTube videos with guys doing this, but it’s obviously for show over simplified, and the quality of retrieval has to be poor or average at best. What are the real best practices in terms of doing this? For example, should you build your knowledge source in a Google sheet that’s one for one matching the vector table structure in Supabase? With metadata for …? Or Use markdown in doc? Predetermine chunk size and edit knowledge source to match desired chunk size instead of relying on “random” chunking? How do you set your raw data up to get the most accurate retrieval from AI agent? (Let’s assume n8n Q&A AI chat agent with Supabase vector and 1,000 rows of unique data from online course material.) Thanks!!

Just dropped a video covering the changes for v2. Who is excited for this update? What features do you wish n8n would add to the new software version. https://youtu.be/7jo3QLVTmyE

I want to give back to the community this weekend. Have a problem with AI, Data, or Freelancing? Like this post, comment posted. And Make a post in the General tab. I will make a Loom for every person that does this.

A few people in here requested this video. I cover the 3 approaches to make this work including: Text, one image, and multiple images. Depending on how this video does, I may cover more image/video/audio AI content on the channel Enjoy! Also post a nano Banana creation down below! https://youtu.be/KTzsO9nMtwI