Quiz on Developing LLM Applications with Python
A diagnostic quiz for Python LLM apps, checking APIs, prompts, LangChain, RAG, embeddings, vector databases, agents, and MCP tool connections.
RAG
Your RAG Demo Seemed Flawless—Until a Real User Arrived.
A four-part LangChain guide shows how to diagnose and fix RAG retrieval failures in chunking, embeddings, query handling, ranking, and data quality.
Stress-Test Your RAG Pipeline Before Users Find Its Weak Points
Small adversarial test sets target synonym, ambiguity, metadata, and multi-hop gaps to expose RAG retrieval failures before users encounter them.
RAG Is Not an Agent — I Created the Bridge from Retrieval to Action
RAG retrieves evidence; agents act. This post tests a bridge layer between separate RAG and agent systems across nine tasks.
Can Text Embeddings Perfectly Capture Text?
vec2text shows embeddings can recover original text, with 92% exact 32-token recovery, challenging privacy assumptions in RAG vector databases.
Python-Based Learning Roadmap for LLM Application Development
A Python roadmap for LLM apps: call APIs, craft prompts, add RAG, build agents, and use MCP while managing reliability, security, and cost.
Building RAG from the Ground Up [Part 5]: A Vector Store Comparison — Pinecone, Qdrant, pgvector, and Weaviate
Learn how 2026 vector stores compare on index types, similarity metrics, hybrid search, and operational tradeoffs when building RAG systems.
Choosing Between RAG and Fine-Tuning for Domain Adaptation: What Fits When
RAG updates context at query time; fine-tuning updates model behavior. Choose RAG for changing facts and fine-tuning for consistent output.