Lessons from one year of AI product building

"Is your team building with AI?"

I've asked this question to dozens of product leaders in the past year. We still get surprised when the answer is: "Absolutely. Our engineers use Cursor. Our PMs prototype in Lovable. We've got Claude in our Slack. We're all-in on AI."

I nod. Then I ask a follow-up: "What AI features are you shipping to your customers?"

That's when the energy shifts. "we have a beta chatbot. Engineering built it during a hackathon. We're waiting to see how users respond."

Here's what I've learned: there's a canyon between using AI tools and building AI products. Most teams are on one side, thinking they're on the other.

Using AI to boost your team's productivity is powerful, we do it too at Lovelaice. But building AI products means integrating AI into your product, bringing it to your customers, creating value through your features. That's a different discipline entirely.

For the teams that have crossed that canyon: shipped a prototype, launched something to users, started the journey, the path forward is surprisingly unclear. We've seen it across 100+ teams and 1,000+ experiments: prompts scattered across codebases, cost models that fall apart at scale, features that work in demos and fail silently in production.

If this sounds familiar, keep reading.

Maybe you've shipped an AI beta and need to productize it, make it reliable, explainable, sustainable for the business.

Maybe AI is on your 2026 roadmap and you want to start right instead of learning everything the hard way.

Either way, what follows are 6 principles we're taking into 2026: lessons earned through a year of building, failing, and iterating alongside teams in the trenches.

You'll learn why latest GPT isn't always the answer, why your power users might bankrupt your AI feature, and the single shift that unlocks 40%+ accuracy gains.

The 6 principles for AI product building in 2026

Principle 1: Domain expertise drives AI quality

What most teams get wrong: Most teams make the same mistake when building their first AI feature: they default ownership to engineering. Engineers choose the model. Engineers write the first prompts. Engineers deploy, set up infra, and iterate.

Meanwhile, product managers and domain experts, the people who understand the user, the workflow, and what “good” actually looks like are brought in after something already exists.

In many cases, PMs don’t even have access to the prompts.

They can’t evaluate outputs before deployment.

They can’t compare models or test variations.

They only see results once engineering has already committed to a direction.

This is backwards.

AI product quality is largely determined before infrastructure decisions are made. It lives in two places:

1. - The system instructions (prompt) — how the problem is framed, constrained, and contextualized
2. - The evaluation — how the team decides what is acceptable, useful, or valuable

Both of these are domain decisions, not engineering ones.

When engineers lead these steps by default, you often end up with something that is technically sound, but contextually lacking.

Data from our practice: A business sustainability team came to us overwhelmed: they'd heard AI could help but didn't know where to start. Their core work was evaluating businesses against a proprietary sustainability framework they'd developed over years. Complex, nuanced, time-consuming.

First iteration: less than 40% accuracy. Unusable.

The turning point? We put their domain experts: the people who'd done these evaluations manually for years, in charge of reviewing outputs and improving the AI prompts. They flagged failures, spotted patterns, and each pattern became a prompt fix.

Three weeks later: over 90% accuracy. On a cost-efficient model, not the latest frontier AI. When they benchmarked against five years of historical data, the AI even caught mistakes in their existing manual ratings.

The technology wasn't the differentiator. The domain expertise was.

How to apply it: Get product managers and domain experts hands-on with prompts and evaluation from day one, not after engineering ships a beta. The people who know what "good" looks like should be shaping the AI's behavior, spotting failure patterns, and turning their expertise into prompt instructions and AI failure evaluations.

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Principle 2: The prompt is the product

What most teams get wrong: Teams treat prompts like placeholder copy: something to polish later once the "real" product work is done. We've seen production prompts that are literally one line: "You are a helpful assistant. Help the user with their request." This is the AI equivalent of product requirements that say "make it good." It's not a strategy, it's an abdication of strategy.

How to spot this in your team**:**

• - Your system prompts live only in the codebase, and no one outside engineering has seen them
• - Prompts are a few generic sentences with no domain context, edge case handling, or examples
• - Your prompt is treated like static documentation, it is never updated
• - You cannot track changes in your prompt and how they have impacted your AI performance: cost, latency, accuracy etc.

Data from our practice: We've run experiments across personalization, product recommendations, and insights generation. The pattern is consistent: even the best frontier models fail spectacularly with weak prompts.

In one experiment, AI-powered product recommendations, every model except GPT-5 scored 0% accuracy with a basic prompt. GPT-5 managed only 60%. With a structured prompt containing domain logic, product context, and edge case handling, multiple models hit 90% accuracy.

Same task. Same test cases. The prompt was the entire difference between failure and success.

Across our experiments, we see even 40% accuracy gains from prompt improvements alone, no model upgrades, no new infrastructure. Just better instructions.

How to apply it: Treat your prompt with the same rigor as product requirements. Structure it with clear logic, domain expertise, explicit edge case handling, and examples of good outputs. Version control it. Review it cross-functionally. Iterate based on real evaluation data.

The prompt isn't documentation of what your AI does. The prompt is what your AI does.

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Principle 3: Model selection is a discovery output, not an input

What most teams get wrong: One of the first decisions teams make is choosing a model, usually based on hype or generic benchmark data. "GPT-5 is the best, let's use that." This is starting with technology instead of starting with the problem.

When GPT-5 was released, thousands of integrations broke. Teams had assumed "newer means better" and switched blindly, only to find their features struggling or failing entirely. GPT-5's additional "thinking" capabilities, great for complex reasoning, actually hurt performance on structured, specific tasks that previous models handled well.

From our practice: We've run experiments across invoice data extraction, product recommendations, and content generation. The pattern repeats: the "best" model on benchmarks is rarely the best for a specific use case.

On data extraction tasks, frontier models used 10-20x more tokens than cost-efficient alternatives, with similar or lower accuracy. In one experiment, GPT-5 achieved 60% accuracy while GPT-4o hit 100% on the same task at a fraction of the cost and latency (4.9 seconds vs 47.6 seconds).

The model that wins on Twitter isn't the model that wins on your problem.

How to apply it: Don't choose your model upfront. Treat model selection as an output of discovery, not an input.

Start by testing your specific use case across multiple models, frontier and cost-efficient options. Let the data tell you which performs best for your problem. You'll often find that smaller, cheaper models outperform expensive ones on structured tasks, while frontier models shine on open-ended reasoning.

The question isn't "what's the best model?" It's "what's the best model for this job?" You can only answer that through experimentation with your own data.

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Principle 4: Systematic validation prevents silent failure

What most teams get wrong: Teams test on two or three happy cases, label the feature "beta," and ship it, waiting for users to do their testing for them. With AI, it's dangerous.

Why? Because AI fails silently.

Traditional software fails loudly: error messages, crashes, broken UI. Users complain, tickets get filed, you know something's wrong. AI always returns something. The response looks confident. Users might not even realize it's wrong: they just don't get value, lose trust, and quietly leave. You never see the failure in your metrics because it doesn't look like failure. It looks like a response.

From our practice: I experienced this firsthand with Miro's AI chatbot. After months away from the tool, I was excited to see a prominent AI feature promising to help with brainstorming. I typed a detailed prompt: session structure, outcomes needed, participant count, format preferences.

What I got back? A formatted text version of my own prompt.

No error. Just confident-looking output that delivered zero value. The AI didn't fail visibly, it failed silently. As a user, my trust was broken. As a product person, I recognized the symptom: a feature that was tested on happy cases but never on real, messy user inputs.

How to apply it: You can uncover 70% of AI failure modes before production through systematic testing, no complex infrastructure required.

Build a comprehensive set of test cases: happy paths, edge cases, conflicting inputs, incomplete data, the messy reality of how users actually interact with your product. Test across multiple prompt versions and models. Have the responses be evaluated and review by your product and customer experts, your PMs. Spot the patterns in AI failures. Fix the failures in your prompt before users ever see them.

The goal isn't perfection. It's catching silent failures before they silently erode trust.

For a deeper dive on building AI features that actually deliver on their promise, see my previous article on impactful AI features.

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Principle 5: Design for AI economics from day one

What most teams get wrong: There's a rule in traditional product development that almost everyone follows: ship the MVP first, optimize later. Prove the market wants it, then worry about efficiency.

This rule might destroy your AI product.

In traditional software, there's near-zero incremental cost per user, your infrastructure costs are mostly fixed. Your power users, the ones who engage 10x more than everyone else, are your most profitable customers.

AI inverts this completely. Every user interaction has incremental cost. Every prompt. Every response. The more your users love your product, the more it costs you to serve them. Your power users become your least profitable users.

From our practice: The Lovable CEO shared a story: a user vibecoded on their platform for 36 hours straight, 1,500 prompts in one session. At an average cost of $0.07 per request (conservative, it could easily be $0.70 with the wrong setup), that's roughly $105 for one user in one day.

The monthly subscription? $20.

One enthusiastic user. One day. 5x the entire month's revenue, gone.

We ran our own experiments on an Airbnb-style personalization feature. GPT-5 (the model everyone defaults to) was 10x more expensive than GPT-4.1, with lower accuracy. At Airbnb's scale, that's the difference between hundreds of thousands and millions per month.

How to apply it: Model your unit economics before you ship, not after you scale. Test your actual prompts across multiple models and measure real token usage, not just API pricing pages. Understand your cost structure so you can design sustainable features and price accordingly.

The "optimize later" advice that works everywhere else? For AI, it means committing to an architecture that costs 10x what it should before you even know it.

For a deeper dive into AI cost economics and how to run discovery on costs before production, see my previous article: Why "Optimize Later" Will Bankrupt Your AI Product.

Principle 6: Start simple, earn complexity

What most teams get wrong: Teams either skip evaluation entirely (ship and pray) or over-engineer from the start, spending months building LLM-as-judge systems, fine-tuning pipelines, and complex orchestration before validating that AI can even solve their problem.

Both approaches fail.

The first group learns their failures in production, from frustrated users. The second group invests months in infrastructure for an approach that might not work and when it doesn't, they've lost time, money, and momentum.

From our practice: The biggest unlock we see is when teams run their first experiments across a few different prompts and a wide array of models. They compare results side by side. They spot better responses immediately. They spot failures just as fast.

One team came to us after shipping an AI feature to customers. In their first experiment on Lovelaice, comparing outputs across models, reviewing responses systematically, they uncovered every failure pattern they'd only learned about through customer complaints. The same insights that took months of production pain? Visible in hours of structured experimentation.

Another pattern: teams assume they need the latest frontier model and complex infrastructure. After testing, they discover a cost-efficient model with a well-crafted prompt matches or exceeds performance, no fine-tuning, no custom training, ready for production in weeks instead of months.

We've seen this across healthtech, fintech, procurement, and data products. The teams that start simple: off-the-shelf models, structured prompts, manual evaluation, move faster and build better than teams that start with complexity.

How to apply it: Start wide, then narrow down. Don't build complexity upfront without first doing the groundwork:

Run experiments across multiple models and prompt variations. Manually review AI responses, not just pass/fail, but actually reading outputs and noting why they fail. Compare side by side. Spot the patterns. Understand which model family works best for your problem, with what accuracy, at what cost.

Only then specialize. Add LLM-as-judge when you can articulate AI failure patterns and what "good" looks like. Fine-tune when you've exhausted what prompting can achieve. Build complex evaluation rubrics when you understand the failure modes you're measuring.

Complexity should be earned through evidence, not assumed from the start. A baseline of ~100 runs across models and prompt iterations will teach you more about your use case than weeks of architecture planning.

This is how you save time, save money, and earn your customers' trust, through reliable, impactful AI products built on a foundation you actually understand.

Your AI product building checklist for 2026

The gap between using AI tools and building AI products isn't closing by accident. It's closing through teams that treat AI as product work, not just engineering work.

Here's what matters:

1. - Domain expertise drives quality. Your product managers and domain experts should own prompts and evaluation from day one. The people who know what "good" looks like are the ones who should shape AI behavior. We've seen this unlock 40%+ accuracy gains without touching infrastructure.
2. - The prompt is your product spec. A weak prompt means every model fails. A structured prompt with domain logic, edge cases, and examples? Multiple models hit 90% accuracy. Version control your prompts. Review them cross-functionally. Iterate based on real data.
3. - Don't choose your model upfront. The "best" model on benchmarks rarely wins on your specific problem. Test across multiple options with your actual use case. Let the data decide. You'll often find cost-efficient models outperform expensive ones on structured tasks.
4. - Test systematically before shipping. AI fails silently. No error messages, no crashes, just confident-looking responses that deliver zero value. Build comprehensive test cases: happy paths, edge cases, messy inputs. Catch 70% of failures before users see them.
5. - Model your economics from the start. Your power users, the ones who love your product most, become your least profitable customers in AI. One enthusiastic user can cost 5x their monthly subscription in a single day. Test real token usage across models before you ship, not after you scale.
6. - Earn complexity through evidence. Start with off-the-shelf models, structured prompts, and manual evaluation. Run ~100 experiments across models and prompt variations. Understand what works and why. Only then add LLM-as-judge, fine-tuning, or complex orchestration.

The bottom line: Most teams will spend 2026 learning these lessons the expensive way: through production failures, blown budgets, and eroded user trust. You now have a year's worth of hard-won insights to skip straight to what works.

The teams winning at AI products aren't the ones with the biggest AI budgets or the fanciest models. They're the ones treating AI like product work, starting with the problem instead of the technology, and validating before they scale.

That's your edge. Use it.