A practical playbook to optimize retrieval pipelines for search and RAG: metrics, chunking, hybrid retrieval, ANN tuning, re-ranking, and efficiency.
Build a practical GraphRAG pipeline: extract a knowledge graph, index nodes and chunks, retrieve local paths and global summaries, and synthesize grounded answers.
Practical blueprint for an AI content personalization API: architecture, endpoints, models, metrics, latency, and safety—built to scale.
A practical, end-to-end guide to designing, deploying, and operating embedding-based similarity search in production.
A practical 2026 guide comparing vector vs. keyword search: principles, pros/cons, costs, evaluation, and when to choose hybrid—with code snippets.
Build a practical multi‑modal RAG system that retrieves from images and text using OCR, captions, CLIP embeddings, and vector search.
