Command Palette
Search for a command to run...
Comments
Join the discussionNo comments yet. Be the first to comment.
DSA with Python
Part 2 of 2
I’ll be posting here daily with what I learned and what problems I solved. Whether you're just starting out or restarting after a break this journey is for you. Let’s learn DSA with Python together
Day 1 – Before DSA, Let’s Rewind Python
Today marks the beginning of this journey not just for me, but hopefully for us. Before jumping straight into Data Structures and Algorithms (DSA), I took a step back and did something very important:I recalled the essential Python concepts that form...
More from this blog
RAG: Retrieval Augmented Generation
RAG is the dominant architecture for production AI applications that need to answer questions about specific documents, recent events, or private data. Understanding it completely — including its fail
Apr 8, 20264 min read
Chunking: Why Document Splitting Determines RAG Quality
Developers often focus on which LLM to use or which embedding model is best. In practice, chunking strategy has a larger impact on RAG quality than either of those choices. A mediocre LLM with good ch
Apr 8, 20264 min read
Vector Databases: Infrastructure for Semantic Search
Vector databases are purpose-built for one operation that general-purpose databases handle poorly: finding the k most similar vectors to a query vector. Understanding why this requires specialized inf
Apr 7, 20264 min read
Semantic Search: How Meaning-Based Retrieval Works
Semantic search is the retrieval mechanism in RAG. Getting it right is the difference between an AI assistant that finds the correct information and one that retrieves garbage and hands it to the LLM.
Apr 7, 20264 min read
Vectors and Embeddings: The Mathematical Foundation of AI Systems
Before you can build a RAG system, design a semantic search engine, or understand why vector databases exist, you need to understand embeddings. This is the concept that makes everything else work. Wh
Apr 5, 20264 min read