Insights

Concise technical notes for production-oriented AI.

Short notes on architecture, quality, and operational decisions across RAG, AI agents, computer vision, and MLOps.

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Engineering Reliable Backends for Agentic AI Systems

Designing reliable agent workflows with asynchronous jobs, durable state, idempotency, validation, memory, and distributed tracing.

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RAG Operations15 min read

Silent Failures: Why Production RAG Systems Degrade Without Throwing Errors

Making hallucination amplification, ranking drift, stale context, and retrieval gaps visible through semantic observability.

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RAG Evaluation13 min read

Evaluating RAG: From Golden Datasets to LLM-as-Judge

Metrics that separate retrieval and generation quality, living golden datasets, and dependable release gates.

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Knowledge Systems27 min read

Beyond Vector Search: Knowledge Graphs, Structured Retrieval, and Intelligent Routing

A production architecture that routes semantic search, exact match, SQL, and knowledge graphs according to the information need.

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Retrieval Engineering31 min read

The Anatomy of Production-Grade Retrieval

An end-to-end treatment of ingestion, chunking, embeddings, hybrid search, rank fusion, reranking, security, and evaluation.

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Document AI36 min read

Building a Local-First OCR + LLM Pipeline for Structured Business Documents

Turning confidential business documents into reliable records through preprocessing, layout analysis, OCR, schema-constrained extraction, and validation.

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RAG & LLM2 min read

RAG vs. Fine-Tuning: Which One Should You Use and When?

A practical comparison across enterprise knowledge, behavior change, freshness, cost, and operations.

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AI Agents2 min read

AI Agent Security: Permissions, Human Approval, and Traceability

How to design safe boundaries, approval points, and auditable workflows for tool-using agents.

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MLOps2 min read

Why Do AI Projects Fail in Production?

The data, deployment, monitoring, and ownership problems that matter beyond the model.

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Computer Vision2 min read

Computer Vision in Manufacturing: Designing a Quality Inspection System

A production approach from cameras and lighting to edge inference, false alarms, and human feedback.

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