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AI / Machine Learning

Deeper machine learning articles — model selection, embeddings, and the core ML concepts behind applied AI systems.

5 articles

July 202610 min read

Hybrid Search and Reranking: Better RAG Retrieval

Boost RAG accuracy with hybrid search and reranking, fusing BM25 keyword search with vector search, then reranking with cross-encoders before generation.

Choosing an Embedding Model: A Practical Benchmark

AI/ML

July 20269 min read

Choosing an Embedding Model: A Practical Benchmark

A practical framework for picking an embedding model for RAG: dimension versus cost versus recall, why MTEB scores can mislead you, and a script to benchmark candidates on your own data.

Semantic Search with pgvector: Skip the Dedicated Vector Database

AI/ML

May 202610 min read

Semantic Search with pgvector: Skip the Dedicated Vector Database

You probably do not need Pinecone. How to build production semantic search with pgvector inside the PostgreSQL you already run, indexes and hybrid search included.

RAG vs Fine-Tuning: An Honest Decision Framework

AI/ML

April 20269 min read

RAG vs Fine-Tuning: An Honest Decision Framework

RAG and fine-tuning solve different problems, and most teams pick wrong. A practical framework for deciding which one your LLM feature actually needs.

Building Production RAG LLM Applications: Architecture, Chunking, and Evaluation

AI/ML

March 202612 min read

Building Production RAG LLM Applications: Architecture, Chunking, and Evaluation

A practical guide to architecting production-grade Retrieval-Augmented Generation systems using LangChain, pgvector, and RAGAS evaluation metrics. Covers chunking strategies, vector store setup, retrieval optimization, and how to measure answer quality in real deployments.

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