<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Semsudin S. | Field Notes</title><description>Essays on shipping enterprise GenAI, AI-driven QA, RAG architecture, and production AI systems.</description><link>https://semsudin.com/</link><language>en-us</language><item><title>Seven years with one client: what repeats me</title><link>https://semsudin.com/blog/seven-years-one-client/</link><guid isPermaLink="true">https://semsudin.com/blog/seven-years-one-client/</guid><description>The same client has kept hiring me across four different projects, from a Selenium migration to a multi-agent GenAI platform. I wrote down what I think drives that.</description><pubDate>Wed, 08 Apr 2026 00:00:00 GMT</pubDate><category>Consulting</category><category>Client Relationships</category><category>Career</category></item><item><title>The cost of self-healing QA (real numbers)</title><link>https://semsudin.com/blog/cost-of-self-healing-qa/</link><guid isPermaLink="true">https://semsudin.com/blog/cost-of-self-healing-qa/</guid><description>I ran AI-driven self-healing on 1000+ tests for a global pet care company. This is what it cost and what it saved.</description><pubDate>Tue, 24 Mar 2026 00:00:00 GMT</pubDate><category>AI-Driven QA</category><category>Test Automation</category><category>Economics</category><category>Playwright</category></item><item><title>Shipping GenAI that survives production</title><link>https://semsudin.com/blog/shipping-genai-reliably/</link><guid isPermaLink="true">https://semsudin.com/blog/shipping-genai-reliably/</guid><description>I architected a multi-agent platform at a global industrial company. This post covers why most enterprise AI never reaches production and what the projects that do reach it have in common.</description><pubDate>Tue, 10 Mar 2026 00:00:00 GMT</pubDate><category>AI Systems</category><category>GenAI</category><category>Architecture</category><category>Production</category></item><item><title>Image promotion: the prod isolation pattern I use for enterprise AI</title><link>https://semsudin.com/blog/image-promotion-not-direct-builds/</link><guid isPermaLink="true">https://semsudin.com/blog/image-promotion-not-direct-builds/</guid><description>Many CI/CD pipelines let production pull whatever the build server last produced. That is fine for a marketing site, but on an enterprise AI platform it leads to 2am rollbacks. This is the image promotion pattern I use on those platforms.</description><pubDate>Wed, 04 Mar 2026 00:00:00 GMT</pubDate><category>DevOps</category><category>CI/CD</category><category>AI Systems</category><category>Container Security</category></item><item><title>AI agents in the QA loop</title><link>https://semsudin.com/blog/ai-agents-in-qa/</link><guid isPermaLink="true">https://semsudin.com/blog/ai-agents-in-qa/</guid><description>This post shows what it looks like when AI agents are integrated into the development cycle for test generation, self-healing tests and PR fixes. In most &apos;AI for QA&apos; tools, the AI is bolted on.</description><pubDate>Sun, 22 Feb 2026 00:00:00 GMT</pubDate><category>AI-Driven QA</category><category>Test Automation</category><category>AI Agents</category><category>Playwright</category></item><item><title>Why we picked pgvector over Pinecone</title><link>https://semsudin.com/blog/why-pgvector-over-pinecone/</link><guid isPermaLink="true">https://semsudin.com/blog/why-pgvector-over-pinecone/</guid><description>Enterprise RAG projects usually start by asking whether they need a dedicated vector database. On the enterprise GenAI platform I worked on, we chose pgvector. This is the decision framework I used.</description><pubDate>Tue, 10 Feb 2026 00:00:00 GMT</pubDate><category>RAG</category><category>pgvector</category><category>Architecture Decisions</category><category>AI Systems</category></item><item><title>The RAG patterns I keep reaching for</title><link>https://semsudin.com/blog/enterprise-rag-patterns/</link><guid isPermaLink="true">https://semsudin.com/blog/enterprise-rag-patterns/</guid><description>This is the decision tree I use when designing retrieval for enterprise AI. I start with vanilla pgvector and only add hybrid search or knowledge-graph traversal when the use case needs it.</description><pubDate>Sun, 18 Jan 2026 00:00:00 GMT</pubDate><category>RAG</category><category>AI Systems</category><category>pgvector</category><category>Graph-RAG</category></item></channel></rss>