AI Product Lessons: Users Want Augmentation, Not Automation
What building AI features for six products taught me: users want their existing tasks done faster, visible uncertainty and easy overrides, not chat.
Thoughts on data engineering, cloud architecture, and technology. 2205 articles and counting.
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What building AI features for six products taught me: users want their existing tasks done faster, visible uncertainty and easy overrides, not chat.
Andriel showed me a Pokémon drawing and I answered without looking up. The one rule I set afterwards: when my kids talk, the phone goes down.
Our Synapse to Fabric migration took twice as long as planned. Five lessons on scope, notebook refactoring, Direct Lake, capacity monitoring and training.
Why I now prefer explicit, predictable code over clever one-liners, the four questions I ask before merging, and when terse code is the better choice.
A layered test strategy for LLM apps: stubbed unit tests per commit, pass-rate gates on PRs, nightly evaluator runs and red teaming before release.
Why we moved Microsoft Fabric from F64 to F32: the three sizing mistakes, the monitoring and alerts we added, and when going smaller is the wrong call.
Most technical posts fail readers with a job to do; here is a five-part template, the gotchas worth including, and when not to use it.
Treat cost per query as the unit metric for an LLM app: decompose it from the usage object, then cut it in order with model choice, caching, context and batch.
What our family has learned about our son's autism meltdowns: what makes them worse, what helps, how we prevent some, and why it isn't bad parenting.
Which LLM signals deserve a dashboard or a page: tokens by feature, latency split by phase, throttling, and sampled quality, using OpenTelemetry on Azure.
Trying to learn every new framework and model leads to fatigue, not growth. Why depth, deliberate rest and relevance beat keeping up.
A practical decision guide for picking Azure Databricks or Microsoft Fabric as your primary data platform in 2026, and when running both is worth the cost.
Running Semantic Kernel agents in production in early 2026: which APIs are GA, which are preview, how to guard tool calls, and where Agent Framework fits.
How I went from working every weekend to zero weekend work: the setup, the team pushback, what changed after eight weeks, and the exceptions I still allow.
Azure AI Search, DocumentDB, Cosmos DB for NoSQL, pgvector, Pinecone and Redis each fail differently in RAG. Where each one hurts in January 2026.
Why I still keep a blog when everyone posts on LinkedIn and X: ownership, clearer thinking, and a searchable record of my work.
Eight prompt engineering myths worth dropping in 2026, from verbose prompts to reflexive chain-of-thought and temperature rules, and what to measure instead.
Andriel needs routine, not just likes it. Living with that has changed how I design functions, error messages, API deprecations and change for my teams.
Diagnose why a RAG system fails before adding patterns: hybrid search, semantic reranking, grounding gates and citations on Azure AI Search, mapped to failures.
My seven-year-old asked why I'm always on my computer. How remote tech work erased my boundaries, and the rules I'm putting back in place.
An honest look at running Microsoft Fabric on an F64: what OneLake and Direct Lake deliver, how throttling really works, and what the bill looks like.
Re-sent history, hidden reasoning tokens, billed failures, idle fine-tuned deployments and re-embedding: where Azure OpenAI bills drift from the estimate.
How I explained ChatGPT to my sons, aged seven and 10: autocomplete, educated guessing, and why sounding confident is not the same as being right.
A practitioner's reality check on AI agents going into 2026: the use cases that work, where they break, and how to pick a framework without the hype.