About
Who writes this, and why
Who writes this
Dhirendra Choudhary — five years across the AI stack, in that order: data scientist first, then ML engineer, then AI engineer working on LLMs and the systems around them. Different domains along the way, so the roadmap here is written from having actually walked most of it, not read about it.
The reason this site exists: the route from "I know how to code" to "I can ship an AI product" is scattered across a hundred blog posts, a dozen frameworks, and a lot of hype. Someone starting today doesn't need another LangChain tutorial — they need the map. This is the map I wish I'd had.
linkedin.com/in/dhirendrachoudhary· hello@aiengineering.guide
What this is — and isn't
It's written lessons, interview cards, coding problems, and case files — free, no paywall, no signup. It goes deep on production reality (evals, cost, guardrails, gateways), the part most guides skip. Every lesson is dated and verified; nothing is a frozen 2023 tutorial dressed up for 2026.
It's not a video course, a cohort program, or a paid bootcamp. It's not aggregator listicles or paper summaries. And it doesn't run its own forum — the good communities already exist, and it links out to them.
Why the eight-part format
Every full lesson is the same eight parts: the problem, the intuition, the mechanism, build it, use it, in production, failure modes, and what you keep. The format is the product. Most tutorials stop when the code works — right before the parts that actually matter at work. Sections six and seven —in production and failure modes — are the wedge, and they are enforced by a build gate, not by willpower: a lesson whose production section is thin does not publish. The little bar chart at the top of every lesson (the "Depth Meter") shows that shape before you read a word.
Where the site is right now
The roadmap and its scaffolding are live; most lessons are still in outline form — the frontmatter, the outcomes, and what each lesson will cover. One lesson is fully written (Design an LLM gateway) as a proof of the format. The rest will fill in stage by stage; nothing is promised on a fixed schedule.
What this deliberately does not cover
- Model training from scratch — this is about engineering around models, not building them.
- Framework tours and changelog-chasing — the mechanisms outlast the libraries.
- Prompt-engineering listicles — where a technique is load-bearing it appears inside a lesson, not as a trick.
- Languages beyond Python in v1 — TypeScript arrives only where the concept is genuinely different.
Where to ask questions
aiengineering.guide doesn't run a forum or comments — the good AI-engineering conversations already happen in a few well-run spaces. If you're stuck on something a page here doesn't cover, one of these is where to go.
- Fast.ai Discord
general ML + AI Q&A, historically the friendliest new-to-ML space
- LangChain Discord
LLM tooling questions — RAG, agents, MCP, framework debugging
- ArizeAI Slack
production LLM evals + observability + drift; MLOps-heavy
- HuggingFace Discord
models, transformers, dataset & training questions
- r/MachineLearning
longer-form async discussion, paper threads, career questions
These are third-party spaces. aiengineering.guide isn't affiliated with them and can't moderate what happens there.
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Corrections are welcome and credited. Emailhello@aiengineering.guide with the lesson slug and what is wrong, or DM me on LinkedIn. A claimed number without a source is a bug; so is a code sample that does not run.
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