Build a Real RAG App 🔥| Day 21 | Cortex Search + LLM (Streamlit & Snowflake)
Build a Real RAG App 🔥| Day 21 | Cortex Search + LLM (Streamlit & Snowflake)
Welcome to Day 21 of the 30 Days of AI Coding Challenge 🚀
For the last few days, we’ve been preparing the building blocks. Today, we connect everything. This is where AI stops guessing… and starts answering using your data.
Welcome to a complete RAG pipeline.
🧠 By End of today’ s challenge we will have knowledge on
How Cortex Search retrieves relevant documents
How those documents are passed to an LLM as context
How to generate grounded, trustworthy answers
How all RAG steps fit together visually
🛠 Prerequisites
Completed Days 1–20 of the challenge (If not, check the earlier videos and complete them first.)
• 30 Days of AI Coding Challenge 🚀 | Build A...
Timecodes
0:00 - Introduction
0:18 - What you will Learn today
0:46 - What is RAG
1:38 - Why AI Coding Challenge
2:00 - Demo of the Vector Embeddings for RAG
4:14 - Code walkthrough : Visual RAG guide
4:41 - Smart Service Selection
5:20 - Retrieval with Cortex Search
6:41 - Update RAG prompt
7:10 - Generation with Cortex LLM
8:08 - Display Results with Source
9:00 - Why RAG is better then Plain LLM
9:48 - Learn more: Snowflake Cortex AI Functions & RAG
10:38 - Outcome of Day 21 of AI Coding Challenge
10:56 - What you will learn tomorrow
👉 Resources
Day 21 challenge guide:
[https://30daysofai.streamlit.app/?day=21]
Snowflake Cortex AI Functions (including LLM functions)
https://docs.snowflake.com/en/user-gu...
Build a Retrieval Augmented Generation (RAG) based LLM assistant using Streamlit and Snowflake Cortex Search
https://www.snowflake.com/en/develope...
Official challenge announcement:
[https://discuss.streamlit.io/t/the-30...]
Code reference (all days):
[https://github.com/sudeepkumar10x/30D...]
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