<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>TheJobPoolConsultants</title><description>Understand intelligence. Build what’s next. A publication about the ideas, systems, and engineering shaping artificial intelligence.</description><link>https://thejobpoolconsultants.github.io/blogs/</link><item><title>How to Release an AI Change Without Guessing</title><link>https://thejobpoolconsultants.github.io/blogs/articles/how-to-release-an-ai-change-without-guessing/</link><guid isPermaLink="true">https://thejobpoolconsultants.github.io/blogs/articles/how-to-release-an-ai-change-without-guessing/</guid><description>Build a versioned release bundle, quality gates, limited rollout, and rollback plan for an AI assistant. Compare task quality alongside latency and failures.</description><pubDate>Mon, 12 Oct 2026 00:00:00 GMT</pubDate></item><item><title>When a Document Assistant Answers Without Evidence</title><link>https://thejobpoolconsultants.github.io/blogs/articles/when-a-document-assistant-answers-without-evidence/</link><guid isPermaLink="true">https://thejobpoolconsultants.github.io/blogs/articles/when-a-document-assistant-answers-without-evidence/</guid><description>Design a document assistant around authorization, source versions, evidence sufficiency, and citation checks rather than confidence alone.</description><pubDate>Mon, 12 Oct 2026 00:00:00 GMT</pubDate></item><item><title>When Caching Makes an AI Answer Fast and Wrong</title><link>https://thejobpoolconsultants.github.io/blogs/articles/when-caching-makes-an-ai-answer-fast-and-wrong/</link><guid isPermaLink="true">https://thejobpoolconsultants.github.io/blogs/articles/when-caching-makes-an-ai-answer-fast-and-wrong/</guid><description>Design scoped, versioned answer-cache keys and invalidation rules. Learn how stale evidence, permission changes, and concurrent fills affect correctness.</description><pubDate>Mon, 12 Oct 2026 00:00:00 GMT</pubDate></item><item><title>Why AI Requests Wait: Bounded Queues and Deadlines</title><link>https://thejobpoolconsultants.github.io/blogs/articles/why-ai-requests-wait-bounded-queues-and-deadlines/</link><guid isPermaLink="true">https://thejobpoolconsultants.github.io/blogs/articles/why-ai-requests-wait-bounded-queues-and-deadlines/</guid><description>Use a worked queue example to understand overload, admission limits, deadlines, retries, and the difference between offered and successful throughput.</description><pubDate>Mon, 12 Oct 2026 00:00:00 GMT</pubDate></item><item><title>Why an AI Agent Creates the Same Ticket Twice</title><link>https://thejobpoolconsultants.github.io/blogs/articles/why-an-ai-agent-creates-the-same-ticket-twice/</link><guid isPermaLink="true">https://thejobpoolconsultants.github.io/blogs/articles/why-an-ai-agent-creates-the-same-ticket-twice/</guid><description>Follow a lost tool response through an agent, policy gate, action ledger, and idempotent service. Learn why a timeout cannot prove an action failed.</description><pubDate>Mon, 12 Oct 2026 00:00:00 GMT</pubDate></item><item><title>Why More AI Replicas Do Not Immediately Fix a Spike</title><link>https://thejobpoolconsultants.github.io/blogs/articles/why-more-ai-replicas-do-not-immediately-fix-a-spike/</link><guid isPermaLink="true">https://thejobpoolconsultants.github.io/blogs/articles/why-more-ai-replicas-do-not-immediately-fix-a-spike/</guid><description>Trace demand through an autoscaling controller, provisioning, model startup, and readiness. Calculate the backlog accumulated before new capacity can help.</description><pubDate>Mon, 12 Oct 2026 00:00:00 GMT</pubDate></item><item><title>Your AI Model Is Fast. Why Are Users Still Waiting?</title><link>https://thejobpoolconsultants.github.io/blogs/articles/your-ai-model-is-fast-why-are-users-still-waiting/</link><guid isPermaLink="true">https://thejobpoolconsultants.github.io/blogs/articles/your-ai-model-is-fast-why-are-users-still-waiting/</guid><description>Trace a slow AI response from client to queue, prefill, decode, and delivery. Learn to investigate a traffic spike before choosing an intervention.</description><pubDate>Mon, 12 Oct 2026 00:00:00 GMT</pubDate></item><item><title>Attention changed how information moves.</title><link>https://thejobpoolconsultants.github.io/blogs/articles/attention-paper-to-product/</link><guid isPermaLink="true">https://thejobpoolconsultants.github.io/blogs/articles/attention-paper-to-product/</guid><description>Understand the Transformer’s central idea, what the original experiments reported, what they did not establish, and why the architecture still matters to engineers.</description><pubDate>Sun, 20 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Before generation, there was representation.</title><link>https://thejobpoolconsultants.github.io/blogs/articles/how-machines-represent-words/</link><guid isPermaLink="true">https://thejobpoolconsultants.github.io/blogs/articles/how-machines-represent-words/</guid><description>From term counts to embeddings: the choices that determine what a language system can compare.</description><pubDate>Sun, 20 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Build the baseline before the pipeline.</title><link>https://thejobpoolconsultants.github.io/blogs/articles/build-a-retrieval-baseline/</link><guid isPermaLink="true">https://thejobpoolconsultants.github.io/blogs/articles/build-a-retrieval-baseline/</guid><description>A dependency-free lexical retrieval experiment that makes tokenization, ranking, and limitations easy to inspect.</description><pubDate>Sun, 20 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Inside a production RAG pipeline.</title><link>https://thejobpoolconsultants.github.io/blogs/articles/production-rag-architecture/</link><guid isPermaLink="true">https://thejobpoolconsultants.github.io/blogs/articles/production-rag-architecture/</guid><description>A reference design that puts source versions, authorization, retrieval quality, and failure handling around generation.</description><pubDate>Sun, 20 Sep 2026 00:00:00 GMT</pubDate></item><item><title>RAG gives a model evidence. Not certainty.</title><link>https://thejobpoolconsultants.github.io/blogs/articles/how-rag-actually-works/</link><guid isPermaLink="true">https://thejobpoolconsultants.github.io/blogs/articles/how-rag-actually-works/</guid><description>Separate retrieval from generation to understand where grounded answers succeed—and where they still fail.</description><pubDate>Sun, 20 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Simple operations. Learned representations.</title><link>https://thejobpoolconsultants.github.io/blogs/articles/neural-networks-learn-representations/</link><guid isPermaLink="true">https://thejobpoolconsultants.github.io/blogs/articles/neural-networks-learn-representations/</guid><description>Follow a signal through a neural network, then follow the error back to understand what training changes.</description><pubDate>Sun, 20 Sep 2026 00:00:00 GMT</pubDate></item><item><title>The agent isn’t one model anymore.</title><link>https://thejobpoolconsultants.github.io/blogs/articles/agent-isnt-one-model/</link><guid isPermaLink="true">https://thejobpoolconsultants.github.io/blogs/articles/agent-isnt-one-model/</guid><description>Models propose. Tools execute. State remembers. The surrounding system makes the loop useful and accountable.</description><pubDate>Sun, 20 Sep 2026 00:00:00 GMT</pubDate></item><item><title>The context is a design decision.</title><link>https://thejobpoolconsultants.github.io/blogs/articles/context-is-a-design-decision/</link><guid isPermaLink="true">https://thejobpoolconsultants.github.io/blogs/articles/context-is-a-design-decision/</guid><description>What a model sees is a product of selection, permissions, and time—not just the size of a context window.</description><pubDate>Sun, 20 Sep 2026 00:00:00 GMT</pubDate></item><item><title>What does it actually mean for a machine to learn?</title><link>https://thejobpoolconsultants.github.io/blogs/articles/what-machines-learn/</link><guid isPermaLink="true">https://thejobpoolconsultants.github.io/blogs/articles/what-machines-learn/</guid><description>Start with the gap between memorizing examples and learning a pattern that survives new data.</description><pubDate>Sun, 20 Sep 2026 00:00:00 GMT</pubDate></item></channel></rss>