A practical guide to turning AI Agents into real services. Covers Tool Calling, Planner/Executor separation, session state management, human-in-the-loop workflows, failure handling, and cost control.
A practical guide to designing RAG systems. Covers document ingestion, chunking, embeddings, vector search, reranking, prompt composition, and evaluation from a real product engineering perspective.
RAG quality starts with data, not the model. This post explains how to choose source documents, clean HTML/PDF/wiki data, attach metadata, and build a production-ready ingestion pipeline.
Chunking and embeddings define the floor of retrieval quality. This post covers chunk size, overlap, heading preservation, code block handling, embedding model selection, and indexing strategy.
Search quality largely defines RAG quality. This post explains dense retrieval, BM25, hybrid search, query rewriting, metadata filtering, and reranking from a practical engineering perspective.
Retrieval is only half of RAG. This post explains how to structure prompts, select and compress context, design citations, and make the system answer safely when evidence is weak.
To move RAG into production, you need quality evaluation, logging, latency tracking, and feedback loops. This post covers retrieval metrics, groundedness, citation accuracy, observability, and operational checklists.
How to build a production-grade AI model inference server with FastAPI and uvicorn. Covers async processing, batch inference, GPU utilization, and Kubernetes deployment.
How to design production AI Agent systems. A practical guide covering the ReAct pattern, Tool Use, Memory management, Multi-Agent orchestration, and safety design.
How to reliably operate LLM-based services in production. Covers cost management, latency optimization, incident response, and monitoring — all from real-world experience.