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For product-minded engineers

AI Product Engineering

Ship AI products people actually keep using.

The distance between an impressive AI demo and an AI product people rely on is engineering — evals, guardrails, latency budgets, failure UX, and iteration on real usage. Ten weeks to cross that distance, ending with a product of your own in users’ hands.

Apply for the next cohortSee the journey
Duration
10 weeks
Commitment
8–10 hours / week
Format
Live online cohort
Cohort
24 seats · pods of 6
Level
Advanced — AI-Native graduate or equivalent experience
A workbench where a figure assembles a glowing lantern while small figures gather toward its light.

Why this course exists

Demos are easy now. Products aren’t.

A weekend is enough to wire a model into an app and record a jaw-dropping demo. Then reality arrives: the feature is wrong 8% of the time, nobody can say which 8%, latency doubles under load, costs spike, and users quietly leave. Most “AI product” failures are engineering failures wearing a model’s costume.

The discipline that separates products from demos — evaluation suites, guardrails, observability for probabilistic systems, UX for graceful failure — exists in scattered blog posts and hard-won team scar tissue. Almost nowhere is it taught end to end.

This course teaches it end to end. You ship an AI feature in week three, put an eval harness around it before you trust it, and spend the back half iterating on real usage data — because retention, not applause, is the honest metric for AI products.

Fit matters

Who this is for — and who it isn’t.

Small cohorts mean we’d rather turn you away than waste your next 10 weeks. Read both lists honestly.

This is for you if

  • Graduates of AI-Native Software Engineering ready to specialize
  • Experienced developers building LLM features at work right now
  • Product-minded engineers with a product idea and the discipline to ship it properly
  • Founders with an engineering background building AI-first products

This is not for you if

  • Beginners or developers without solid engineering fundamentals — start earlier on the path
  • Anyone wanting ML research or model training; this is the applied product layer
  • Prompt-tinkerers looking for tricks rather than an engineering practice
  • Anyone unwilling to put their project in front of real users by week seven

Expected outcomes

What you’ll be able to do.

Outcomes are written as capabilities, because that’s what reviews test and what employers probe.

  1. 01

    Ship LLM-powered features with structured outputs, cost and latency budgets, and honest failure UX

  2. 02

    Build evaluation suites that gate releases — so “it seems better” becomes a measurement

  3. 03

    Design and ship agent- and retrieval-based features where they genuinely earn their complexity

  4. 04

    Instrument AI features and iterate on real usage, not vibes

  5. 05

    Take one product from idea to users and defend its metrics at the Summit

Skills acquired along the way

LLM engineering

  • LLM APIs & structured outputs
  • Prompting as an engineering artifact
  • Cost & latency budgeting
  • Model selection & fallbacks

Trust & evaluation

  • Eval suite design
  • Guardrails & failure UX
  • Observability for AI features
  • Release gating

Product craft

  • Agents & tool use
  • Retrieval (RAG) done honestly
  • Usage instrumentation
  • Iteration & retention thinking

The learning journey

3 phases. 10 weeks. One ascent.

Every phase has an entry state, an exit state, and a Waypoint Review gating the way forward — so progress is never a feeling, it’s a fact.

  1. 1 · From Model to Feature

    Weeks 1–3

    The applied LLM layer: APIs, structured outputs, prompting as engineering, and the budgets — cost, latency, failure — that product work demands.

    You arrive
    You can engineer software; models are still magic boxes.
    You leave
    You’ve shipped a real AI feature inside an app, within budgets you set on purpose.

    Waypoint 1 · The Shipped Feature

    Demo an AI feature living inside a real application: structured outputs, error handling, a cost/latency budget — and the receipts that it holds them.

  2. 2 · Trust the Output

    Weeks 4–6

    The heart of the course: evaluation. Build the harness that tells you whether the feature works, wire in guardrails, and make failure a designed experience.

    You arrive
    Your feature works — you think.
    You leave
    You can prove how well it works, catch regressions before users do, and fail gracefully.

    Waypoint 2 · The Gated Release

    Show a release blocked, then passed, by your own eval suite. Present the eval design: what it measures, what it misses, and why the trade-off is right.

  3. 3 · Products People Keep

    Weeks 7–10

    Advanced capabilities where they earn their keep — agents, retrieval — and the discipline of iterating on real users. Summit: your product, your metrics.

    You arrive
    One trustworthy feature.
    You leave
    A product with users, instrumentation, and a defended retention story.

    Waypoint 3 · Summit — The Product Defense

    Present your product with its usage data: what users did, what you changed, what retained. Defend the engineering and the product calls together.

The curriculum, week by week

Every week has a theme. Every Friday, something ships.

Lesson-level detail is refined with each cohort; the weekly structure below is the commitment.

Weeks 1–3From Model to Feature

  1. Wk 01

    LLM APIs in anger: tokens, context, structured outputs, streaming

    Ships: A working feature spike with structured output parsing

  2. Wk 02

    Prompts as engineering artifacts: versioned, tested, reviewed

    Ships: Prompt suite under version control with regression cases

  3. Wk 03

    Budgets: cost, latency, and designing for the failure case

    Ships: The Shipped Feature, ready for Waypoint 1

Weeks 4–6Trust the Output

  1. Wk 04

    Eval design: golden sets, LLM judges, and measuring the unmeasurable

    Ships: A first eval suite scoring your live feature

  2. Wk 05

    Guardrails and failure UX: designing what happens when the model is wrong

    Ships: Guardrails and honest fallback paths shipped

  3. Wk 06

    Observability: tracing, feedback capture, and release gating in CI

    Ships: The Gated Release, ready for Waypoint 2

Weeks 7–10Products People Keep

  1. Wk 07

    Agents and tools: when autonomy helps and when it hurts

    Ships: An agent or tool-use capability shipped where justified — and users in the product

  2. Wk 08

    Retrieval done honestly: when RAG earns its complexity

    Ships: Retrieval layer shipped or consciously rejected, with rationale

  3. Wk 09

    Reading usage: instrumentation, feedback loops, and what to build next

    Ships: Iteration shipped in response to real usage data

  4. Wk 10

    The product defense: metrics, story, and the roadmap you’d bet on

    Ships: Summit Defense passed; case study published

Projects

The work you’ll point to later.

Every phase produces something reviewable; the Summit produces something you defend.

Phase 1

The Shipped Feature

An LLM feature inside a real app with structured outputs and budgets it provably holds.

Phase 2

The Gated Release

An eval suite, guardrails, and observability wrapped around your feature — trust as infrastructure.

Summit project

Your product, with users

One AI product taken from prototype to real usage, instrumented, iterated, and defended with metrics.

Mentorship

A system, not a Slack channel.

Small pods, senior mentors, and promises with numbers attached — mentorship here is a system, not a Slack channel.

Pods of six

You’re placed in a pod of six in week one and climb with them to the end. Small enough that your absence is noticed; strong enough to survive a hard week.

Weekly pod studio

A live session with your pod mentor every week: demos, unblocking, and group review of real work.

1:1 every two weeks

Thirty minutes with your mentor, agenda owned by you — career, code, or the thing you’re stuck on.

Code review in 24 hours

Every weekly deliverable gets a real review — line comments and questions — within one day. That’s a promise, not an aspiration.

Open office hours

Twice a week, drop in with anything. No booking, no agenda.

On this course

Mentors have shipped LLM features in production and review your evals the way they review their own teams’.

Also on this course

Weeks 7–10 add a weekly product review alongside code review — usage data on the table, roadmap decisions defended.

The Ship Week Rhythm

Weeks don’t drift here.

Courses fail when weeks drift. Ours can’t: every week ends with something shipped, and going quiet triggers a human, not a reminder email.

  1. 01

    Monday goals

    You post the week’s targets to your cohort space. Everyone sees them.

  2. 02

    Async standups

    Three times a week, two sentences: what moved, what’s stuck.

  3. 03

    Friday ship

    The week’s increment goes up for review and gets demoed in your pod. Shipped beats perfect.

  4. 04

    The 48-hour rule

    Silent for two days? A mentor reaches out personally. Not a bot — a person who knows your project.

  5. 05

    The build log

    Your public record of the whole journey — and the proof of work employers actually read.

Waypoint Reviews

No exams. Real reviews.

No exams. Each phase ends with a Waypoint Review — you present working software against a rubric published on day one, the way real engineering teams review real work.

A working demo, defended

Forty-five minutes: you demo the milestone, walk the code, and answer questions about why it’s built the way it’s built.

A rubric you can read on day one

Functionality, code quality, AI-collaboration quality, and reasoning under questions. No surprises, no trick questions.

AI collaboration is graded

Prompt hygiene, verification discipline, knowing when not to use the tool — assessed explicitly, because that’s the craft now.

Revise and resubmit

Miss a waypoint and you get a week to close the gap, like a returned pull request. Rigorous, not punitive.

You graduate when

  • All three Waypoint Reviews passed
  • Summit product live with real users and instrumentation
  • Build log complete for at least 90% of program weeks
  • Product case study with metrics published

From the trail

“My demo looked perfect and my eval suite said 71% on the cases that mattered. Learning to close that gap — and to measure it at all — changed how our whole team ships AI features now.”

Product engineer · AI Product Engineering

Pricing

One price. Everything included.

No fake discounts, no upsells inside the course, no “premium tier” of attention. Everyone gets the whole thing.

AI Product Engineering

₹75,000

Or 3 monthly installments of ₹26,000

One-time, all-inclusive.

Apply now

Every cohort reserves need-based scholarship seats. If the price is the only thing stopping you, apply anyway and say so.

What’s included

  • Ten weeks of live studio sessions, recorded
  • A pod of six and a mentor who ships LLM products
  • Code review and weekly product review from week 7
  • Three Waypoint Reviews and the product defense
  • Lifetime access to materials and the alumni network

Questions, answered straight

FAQs

Do I need to have taken AI-Native Software Engineering first?

It’s the intended path, but equivalent experience works: you should be comfortable designing and shipping full systems and using AI tools in your daily workflow. If you’re unsure which applies to you, write to us and we’ll tell you honestly.

Will this teach me to train or fine-tune models?

No — this is the product layer: using models brilliantly, not building them. Fine-tuning appears only where a product genuinely calls for it. If you want research or training pipelines, this isn’t your course.

Where do the “real users” come from?

From week seven your product must be in front of people who aren’t classmates — your network, a community, an existing audience, or your workplace. Mentors help you scope a product where finding first users is feasible; “I’ll get users later” fails the Summit rubric by design.

Can I build on my employer’s product instead of my own?

Yes, if you have permission and can present it in reviews. Several structures work — a feature inside your company’s app, an internal tool with real internal users. The eval and iteration requirements stay identical.

What about API costs?

Budgeting is literally part of the curriculum — projects are scoped so typical API spend stays modest (comparable to a streaming subscription), and cost discipline is one of the graded skills.

Are live sessions recorded?

Every session is recorded and available the same day. Live attendance is strongly encouraged — the studio format is interactive — but the program is built to survive real life and real time zones.

What time zones do cohorts run in?

Cohorts are scheduled around Indian evenings and weekend mornings (IST), which also works for the Gulf and Southeast Asia. Everything is recorded, and async reviews mean your work never waits for a meeting.

What is the refund policy?

Full refund within the first two weeks of the cohort, no questions and no forms-designed-to-exhaust-you. After that, fees are non-refundable but you can defer to a later cohort once, free.

Do you offer installment plans and scholarships?

Yes — installment options are listed with each course’s pricing, and every cohort reserves need-based scholarship seats. If price is the only thing stopping you, apply and say so plainly.

Will I get a certificate?

Graduates receive a completion credential — but the artifacts that actually open doors are the ones you build: a public build log, defended projects, and a published case study. We optimize for those.

How do I know which course to start with?

Working developer → AI-Native Software Engineering. New to code → Software Foundations. Already AI-native and choosing a depth → the specializations. Bringing a whole team → For Teams. Still unsure? Write to us with two lines about where you are; a human replies.

Cohorts are capped at 24

Ready for AI Product Engineering?

Applications take ten minutes and are read by an engineer. If a different course fits you better, we’ll tell you straight.

Apply nowor write to a human — hello@upskillcamp.com