<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Posts on Tyler Wells</title><link>https://blog-theta-seven-23.vercel.app/posts/</link><description>Recent content in Posts on Tyler Wells</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 24 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog-theta-seven-23.vercel.app/posts/index.xml" rel="self" type="application/rss+xml"/><item><title>What I Learned Building a Multi-Agent Document Analysis System</title><link>https://blog-theta-seven-23.vercel.app/posts/multi-agent-doc-analysis-post-4-lessons-learned/</link><pubDate>Fri, 24 Apr 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/multi-agent-doc-analysis-post-4-lessons-learned/</guid><description>[Multi-Agent Series 4/4] What worked, what broke, how chunking caused false conclusions, and what I&amp;#39;d do differently in v2.</description></item><item><title>Coordinating Multiple LLM Agents: Cross-Domain Synthesis</title><link>https://blog-theta-seven-23.vercel.app/posts/multi-agent-doc-analysis-post-3-coordinator/</link><pubDate>Thu, 23 Apr 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/multi-agent-doc-analysis-post-3-coordinator/</guid><description>[Multi-Agent Series 3/4] How a coordinator turns specialist findings into cross-domain insights and decision-ready recommendations.</description></item><item><title>Building Specialist LLM Agents: Technical, Risk, Cost, and Timeline Analysis</title><link>https://blog-theta-seven-23.vercel.app/posts/multi-agent-doc-analysis-post-2-specialists/</link><pubDate>Wed, 22 Apr 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/multi-agent-doc-analysis-post-2-specialists/</guid><description>[Multi-Agent Series 2/4] How specialist prompts, shared inheritance, and structured JSON outputs turn one LLM into multiple domain-specific analyzers.</description></item><item><title>Why Multi-Agent Systems Beat Single Agents for Complex Documents</title><link>https://blog-theta-seven-23.vercel.app/posts/multi-agent-doc-analysis-post-1-why-multi-agent/</link><pubDate>Tue, 21 Apr 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/multi-agent-doc-analysis-post-1-why-multi-agent/</guid><description>[Multi-Agent Series 1/4] Why one large prompt breaks down on RFPs and contracts, and how specialist agents plus coordinator synthesis produce better analysis.</description></item><item><title>Building Ozark Ridge: Lessons Learned and What I'd Do Differently</title><link>https://blog-theta-seven-23.vercel.app/posts/ozark-ridge-post-5-lessons-learned/</link><pubDate>Thu, 16 Apr 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/ozark-ridge-post-5-lessons-learned/</guid><description>[Retail AI Series 5/5] What worked, what didn&amp;#39;t, what I&amp;#39;d do differently in v2, and why this project matters for e-commerce AI.</description></item><item><title>Building the AI Product Assistant: Context Injection, Multi-Turn Chat, and Cross-Product Retrieval</title><link>https://blog-theta-seven-23.vercel.app/posts/ozark-ridge-post-4-ai-assistant/</link><pubDate>Wed, 15 Apr 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/ozark-ridge-post-4-ai-assistant/</guid><description>[Retail AI Series 4/5] How to build a Rufus-style AI assistant that answers product questions, suggests complementary gear, and builds camping loadouts — with conversation history, context injection, and dynamic retrieval.</description></item><item><title>Keyword Search vs Semantic Search: Why Natural Language Queries Need Vector Embeddings</title><link>https://blog-theta-seven-23.vercel.app/posts/ozark-ridge-post-3-keyword-vs-semantic/</link><pubDate>Tue, 14 Apr 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/ozark-ridge-post-3-keyword-vs-semantic/</guid><description>[Retail AI Series 3/5] Side-by-side comparison of keyword and semantic search, why keyword search fails on natural language queries, and what the retrieval scores actually tell you.</description></item><item><title>Building the Catalog and Ingestion Pipeline: Archetypes, Embeddings, and ChromaDB</title><link>https://blog-theta-seven-23.vercel.app/posts/ozark-ridge-post-2-catalog/</link><pubDate>Mon, 13 Apr 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/ozark-ridge-post-2-catalog/</guid><description>[Retail AI Series 2/5] How to generate 1180 realistic products from 20 archetypes, why product descriptions matter for RAG, and how the ingestion pipeline works.</description></item><item><title>Building AI Search for a Retail Website: The Stack and Why</title><link>https://blog-theta-seven-23.vercel.app/posts/ozark-ridge-post-1-stack/</link><pubDate>Sun, 12 Apr 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/ozark-ridge-post-1-stack/</guid><description>[Retail AI Series 1/5] Building a mock outdoor retail site with AI-powered product search and a Rufus-style assistant. This post covers the architecture, stack decisions, and why RAG matters for e-commerce.</description></item><item><title>AI-Powered QA Testing with playwright-cli and GitHub Copilot</title><link>https://blog-theta-seven-23.vercel.app/posts/playwright-cli-qa-post/</link><pubDate>Thu, 09 Apr 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/playwright-cli-qa-post/</guid><description>How to use playwright-cli with GitHub Copilot as an autonomous QA agent — without Playwright MCP, without writing test code, and without needing Copilot Vision or an embedded browser.</description></item><item><title>What I Learned Building a LangGraph Agent From Scratch</title><link>https://blog-theta-seven-23.vercel.app/posts/langgraph-agent-post/</link><pubDate>Mon, 30 Mar 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/langgraph-agent-post/</guid><description>Building a LangGraph job research agent showed me where agent loops actually help, why typed state matters, and how conditional edges change the design.</description></item><item><title>Your MCP Server Is Only as Good as Its Docstrings</title><link>https://blog-theta-seven-23.vercel.app/posts/building-a-cfb-mcp-server/</link><pubDate>Sun, 15 Mar 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/building-a-cfb-mcp-server/</guid><description>Building a college football data MCP server taught me that the most important design decision in an agentic system isn&amp;#39;t the architecture — it&amp;#39;s the docstrings.</description></item><item><title>How a Simple Power Automate Workflow Automated 250+ Hours of Work Per Month</title><link>https://blog-theta-seven-23.vercel.app/posts/retail-power-automate-workflow/</link><pubDate>Fri, 20 Feb 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/retail-power-automate-workflow/</guid><description>Eliminate costs tied to repetitive corporate tasks with simple workflows like this -- a Microsoft Form + a Power Automate flow + an AI prompt</description></item><item><title>Scoring RAG Answer Quality with an LLM Judge</title><link>https://blog-theta-seven-23.vercel.app/posts/rag-answer-judging/</link><pubDate>Mon, 26 Jan 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/rag-answer-judging/</guid><description>[RAG Series 3/3] Source URL retrieval tells you whether the right content was retrieved. It doesn&amp;#39;t tell you whether the answer was any good. Adding an LLM judge to the eval harness reveals two failure modes that retrieval scoring alone can&amp;#39;t see.</description></item><item><title>How to Design RAG Eval Test Cases</title><link>https://blog-theta-seven-23.vercel.app/posts/design-rag-eval-test-cases/</link><pubDate>Sat, 24 Jan 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/design-rag-eval-test-cases/</guid><description>[RAG Series 2/3] How to write test cases that catch real retrieval problems, why source URL retrieval is a useful proxy metric, and when it isn&amp;#39;t enough.</description></item><item><title>RAG Retrieval: Chunking, Embeddings, Reranking, and an Eval</title><link>https://blog-theta-seven-23.vercel.app/posts/rag-retrieval-quality/</link><pubDate>Thu, 22 Jan 2026 00:00:00 +0000</pubDate><guid>https://blog-theta-seven-23.vercel.app/posts/rag-retrieval-quality/</guid><description>[RAG Series 1/3] Covers chunking strategy, embedding model consistency, reranking, and building an eval harness — including what happened when Voyage AI&amp;#39;s free-tier rate limits forced a more resilient architecture.</description></item></channel></rss>