Practical AI/ML content. Mentor-curated. No fluff.
"How does an LLM generate a response?" sounds like a warm-up question. It isn't. Here's why most answers stall after two beats, and what the full answer actually requires.
July 19, 2026
Token count, not word count, is what you're billed for — and the gap between the two is bigger than most engineers assume. A framework for measuring prompt cost before it shows up as a surprise on the invoice.
July 19, 2026
Interviewers ask one open-ended question about production prompt design and listen for five specific things. Here's what those five things actually are, and which one most candidates skip.
July 19, 2026
Role, Context, Task, Format — the RCTF framework for writing prompts that hold up in production, plus the measured cost of skipping the Format field: an 80% format-drift rate.
July 19, 2026
Interview questions about tool use and agent loops aren't really testing whether you can name the steps — they're testing whether you've watched one fail. Here's what the questions are really probing for.
July 19, 2026
Every AI agent that takes real actions relies on a boundary most people never name explicitly: the model requests, your code decides. Here's why that boundary is the whole design, and what breaks when it's missing.
July 19, 2026
A single AI agent fails loudly. A team of them can fail silently — passing garbage from one agent to the next while reporting success. Here's the design pattern that catches both failure modes before production does.
July 19, 2026
Multi-agent system design has become one of the default AI engineering interview topics. The question isn't whether you can name the pattern — it's whether you can explain what breaks it.
July 19, 2026
Production AI safety interview questions have a shape: six specific beats, and most candidates only cover four of them. Here's what the missing two reveal about your answer.
July 19, 2026
Guardrails, evals, and observability aren't three separate projects — they're three layers of the same safety stack, and most teams only build the first one. Here's why that's not enough.
July 19, 2026
Most model-selection debates start with a model name. The ones that hold up under scrutiny start with a constraint. Here's the framework, with real cost numbers.
July 19, 2026
Interviewers don't want a model name. They want to hear you name a constraint first. Here's why that one habit is the whole signal — and what it reveals about fine-tuning, benchmarks, and production trade-offs.
July 19, 2026
A career-switcher guide to AI portfolio projects: how engineers, data analysts, PMs, and ML-adjacent professionals can choose projects that make past experience relevant.
July 19, 2026
A practical AI/ML resume project list: RAG, agents, evals, streaming APIs, SageMaker, monitoring, and other portfolio builds hiring teams can inspect.
July 19, 2026
A practical roadmap for building an AI portfolio from zero experience: start with small LLM tools, add RAG, evals, deployment, and a public project narrative.
July 19, 2026
An LLM has no memory of its own — every call only sees what you send it. Here's why a bigger context window doesn't solve that, and the three strategies that actually do.
July 15, 2026
Running a model in a Jupyter notebook proves nothing. Here's how to deploy a real SageMaker endpoint with autoscaling — using boto3 and the SageMaker SDK.
June 14, 2026
Most teams jump to fine-tuning too early. Here are the 3 questions to ask first, when prompting wins, and a Python benchmark to measure the difference yourself.
June 10, 2026
Retrieval-Augmented Generation is everywhere. But most tutorials skip the tradeoffs. Here's what actually matters when you're building production RAG.
June 8, 2026
ML teams consistently over-permission their AWS roles. Here's how execution roles actually work, what permissions SageMaker jobs actually need, and a Terraform module to create it right.
June 5, 2026
Certificates don't get you hired. Deployed projects do. Here's the difference between passive and active learning in AI.
June 1, 2026