Most teams make the same mistake when they “start doing AI.” They treat it like a model problem first.

In practice, the winning teams treat it like a product + systems problem first. The model matters, but you are supposed to usually rent it. What you own is the workflow around it: what the user sees, what gets measured, how mistakes get caught, and how the system improves without lighting your support queue on fire.

If you want a simple mental model, use this: AI engineering is the discipline of turning unpredictable model behavior into a reliable product.

Almost every AI application collapses into three layers:

1) Application development This is the product. Interface, user experience, prompt/context construction, tool use, guardrails, and evaluation loops. This layer is where most AI apps win or lose.

2) Model development Training, fine-tuning, dataset engineering, inference optimization. Some companies live here. Most don’t need to, at least at the start.

3) Infrastructure Serving, orchestration, compute, monitoring, logging, incident response, cost controls.

A lot of teams start in layer 2 because it feels “technical.” Then they discover their real bottleneck was layer 1 all along: unclear requirements, messy user flows, no measurement, and no feedback loop.

Traditional ML engineering is often about building a model that outputs a specific thing you can compare to a ground truth. With foundation models, you’re working with systems that produce open-ended outputs. That changes the job in three big ways:

You’re adapting more than you’re training. Instead of “build model → ship,” the loop becomes “adapt model → evaluate → ship → learn from usage → adapt again.”

Compute and latency stop being background details. Foundation models are expensive and slower. Tokens are generated sequentially, so output length directly affects latency and cost. This is why inference optimization is suddenly a front-page concern instead of a niche specialty.

Evaluation becomes harder, but more important. With open-ended outputs, you can’t always maintain a neat list of “correct answers.” You need better test sets, better rubrics, and production telemetry that tells you when quality is sliding.

The practical takeaway: AI engineering is the business of measurement. If you can’t measure “good,” you can’t ship safely.

Before you build anything, answer a blunt question: what happens if we don’t do this?

A useful way to categorize use cases is by the level of risk/opportunity:

Existential risk: competitors using AI could make you obsolete. This is common in document-heavy and information-heavy workflows. Some research tries to quantify which jobs/tasks are most exposed to LLM capabilities. 1.

Profit and productivity: you’ll miss efficiency gains, lower support costs, higher conversion, faster sales ops, better retention. 1.

Exploration: you’re not sure where AI fits yet, but you don’t want to be the company that waited too long.

If you’re in bucket (3), that’s fine. Just be honest that you’re paying for learning. Don’t pretend it’s a guaranteed product ROI on day one.

A lot of “AI product failures” are really “human placement failures.”

You have three common patterns:

AI suggests, human decides. Great for early phases, great for risk control.

AI handles easy cases, escalates the rest. Good middle ground if your routing is solid.

AI responds directly. Highest leverage, highest risk.

A clean rollout usually looks like crawl → walk → run:

Crawl: human involvement is mandatory.

Walk: AI directly helps internal employees.

Run: AI interacts directly with end users.

The key is that “run” is not a vibe, it is something that is earned… If you can’t quantify quality, you’re not ready for direct user-facing automation.

Here’s what teams forget sometimes - a chatbot can answer more messages and still make users unhappier.

So you define thresholds up front. The simplest set is:

Quality: how good does it have to be to count as useful?

Latency: what response time will users accept in this context?

Cost: what’s the allowable cost per request?

Satisfaction: are users actually happier, or just processed faster?

Latency is relative. If humans currently respond in an hour, “a few seconds” can feel magical. If your product normally reacts in 100ms, a few seconds feels broken. Same model, different user expectations.

People casually say “we trained it” when they mean completely different things.

Prompting / context construction: adaptation without changing weights. Faster to iterate, less data needed, great for early product discovery.

Fine-tuning: changes weights. More engineering and data work, but can improve consistency, style, and sometimes latency/cost tradeoffs.

Pre-training: training from scratch, massively resource-intensive and high-risk. It’s a different sport.

This matters because it changes what you should invest in. Many teams are better served by tighter evaluation + better context + better UX than by jumping into fine-tuning.

There’s a hard truth about building on foundation models:

If the underlying model gets better, parts of your product can get absorbed.

A wrapper that exists only because “the base model can’t do X yet” is fragile. Today it’s PDFs. Tomorrow it’s better PDF parsing. Your differentiation disappears and you’re left competing on distribution or price.

A more realistic view of AI competitive advantage is:

Technology: increasingly commoditized for many use cases.

Distribution: big companies often win here.

Data: nuanced, but powerful if usage creates a feedback loop that improves the product over time.

If you can’t win on distribution, your best bet is usually: narrow focus + strong user feedback loop + rapid iteration.

The most dangerous moment in an AI project is “it works in the demo.”

Real products live in maintenance:

Model providers change pricing and behavior.

Context windows get longer, outputs get better, costs shift.

Regulations can change what you can ship, where you can host, and what data you can touch.

Your user base changes, and edge cases become your daily reality.

So you invest in boring infrastructure: versioning, eval harnesses, monitoring, rollback paths, and a process to treat prompt/context changes like production changes.