Deep dives across data, automation & AI

Chunking Strategies: How to Split Documents for Better Retrieval

Master four proven document chunking techniques that dramatically improve AI retrieval accuracy. Learn when to use fixed-size, semantic, sentence-based, and structure-aware approaches with hands-on examples.

Building a RAG Pipeline with Your Own Documents

Learn to build a production-ready RAG system that ingests your real-world documents, creates semantic search capabilities, and generates accurate answers with proper source attribution.

Vector Databases Compared: Pinecone vs Weaviate vs pgvector for Production RAG

Master the critical decision of choosing between Pinecone, Weaviate, and pgvector for your RAG system. Learn implementation patterns, performance benchmarking, and when each database excels in production.

RAG Fundamentals: Build Your First Retrieval-Augmented Generation System

Learn how RAG combines document retrieval with AI generation to create accurate, source-backed responses. Build a complete RAG system from scratch with practical Python examples.

Deploying LLM Applications: API Design and Infrastructure

Learn to build production-ready LLM applications with proper API design, infrastructure scaling, cost management, and monitoring. From local development to cloud deployment.

Building AI Workflows with LangChain and LlamaIndex

Testing and Evaluating LLM Applications: A Comprehensive Guide to Quality Assurance

Master the art of testing non-deterministic LLM applications with rule-based validation, model-based evaluation, automated testing pipelines, and production monitoring strategies that catch problems before they impact users.

Cost Optimization: Token Counting, Caching, and Model Selection for LLMs

Master practical techniques to reduce your LLM API costs by 60-80% while maintaining quality. Learn token tracking, intelligent caching, and strategic model selection with hands-on examples.

Streaming Responses and Real-Time AI Interfaces

Building Streaming AI Interfaces: Real-Time Response Delivery

Learn to build responsive AI applications that stream tokens in real-time, creating conversational experiences that engage users from the first word.

Building a Document Q&A System with Embeddings: A Complete Beginner's Guide

Learn to build an AI-powered document Q&A system from scratch using embeddings and language models. No prior AI experience required—just practical Python skills and clear explanations.

Building a Production Document Q&A System with Vector Embeddings

Learn to build a scalable document Q&A system using embeddings and RAG. Covers advanced chunking, vector search optimization, prompt engineering, and production deployment with real-world regulatory compliance examples.