A practical loop for building AI agents with Python
Start with a small, inspectable agent loop that chooses tools, observes results, and knows when to stop.
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RAG Architecture Deep Dive: Vector Embeddings, Hybrid Search & Reranking
A production engineering guide to building accurate Retrieval-Augmented Generation pipelines using dense embeddings, BM25 keyword matching, and cross-encoder rerankers.
RAG, explained as a retrieval pipeline
A grounded introduction to chunking, embeddings, retrieval, and the evaluation work that makes RAG useful.
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AI
Practical patterns and foundations for building with artificial intelligence.
6 articlesGenAI
Generative models, prompts, evaluation, and production workflows.
2 articlesAI Agents
Tool use, orchestration, and reliable agentic systems.
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Code, libraries, and workflows for useful software.
2 articlesRAG
Ground language models in trusted, useful information.
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Connect AI applications to tools and context with open protocols.
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A practical loop for building AI agents with Python7 minRAG Architecture Deep Dive: Vector Embeddings, Hybrid Search & Reranking11 minRAG, explained as a retrieval pipeline6 minModel Context Protocol (MCP) Guide: Standardizing AI Tool Integration8 minMCP for developers: connect tools through a shared protocol5 minModern Async Python for AI Engineers: Handling High Concurrency7 min
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