Content Engineering lessons we’re taking into 2026

Written by Lisette Walberer and Grace Boatwright | Jan 2026

For the Content Design team at Thumbtack, our work heading into 2026 looks very different from what it did a year ago. Our core skills haven’t changed, but how we apply them has.

We’ve moved from writing product content ourselves to primarily designing AI systems that generate it for us — roughly 25 to be more specific, many of which were 0–1 features developed over the course of the whole year.

For our small team of 5, we’ve learned how to navigate a new challenge this kind of scale has introduced. Generating content with AI is easy. Generating content that’s accurate, useful, on-brand, and predictable with AI is not.

Bridging that gap became our focus in 2025. It’s what led us to formalize what we call content engineering.

What we mean by content engineering

Content engineering = content design + prompting strategies + evaluation.

• Content design: understanding users, context, constraints, and voice
• Prompt strategy: shaping model behavior through structure, examples, and language
• Evaluation: defining quality and enforcing it at scale

Like any design process, content engineering isn’t linear. It’s a loop: clarify → design → test → evaluate → adjust → repeat. The key difference from traditional content design is that you’re no longer producing final copy. You’re designing a system that does.

How it’s playing out in practice

Across all of these features, a few lessons have held true:

It all starts with classic content design. Before writing a prompt, we explore what the ideal content should be the old-fashioned way (in Figma and with users). This content then becomes the examples we feed the model as well as the backbone of our prompt engineering strategy — unlocking strategy around persona, structure, and even complex logic.

Design inputs as carefully as outputs. As the old adage goes, “garbage in, garbage out.” In other words, great outputs require the right inputs. Identifying ideal content helps surface the data needed to personalize it. This “context engineering” step is critical — and one content designers should be deeply involved in.

Plan for iteration. Prompts are finicky. What works in one project may fail in another. Examples can outperform written rules — or cause hallucinations. Over time, you learn how to iterate well by recognizing common model failure patterns.

Set a minimum quality bar. Without a clear quality bar, prompt iteration becomes endless. Conveniently, the bar is hiding in plain sight. We use the ideal content we put together to inform the strategy to develop our evaluation criteria. Then, we hold ourselves accountable to them because the same standards we apply to human-written content apply to AI-generated content as well.

Know where humans matter most. LLMs struggle with nuance and competing priorities. Strategic judgment — especially balancing user and business needs — has to come from content designers. Models can execute well only when humans have strategised well.

What we’re carrying forward

Across projects, the pattern is clear: ideal content shapes model behavior, evaluation drives quality, and prompts are design artifacts. Content engineering isn’t about writing faster — it’s about building systems that scale responsibly, behave predictably, and meet the same standards we expect from human-written content.

The work is demanding, but the impact is real — and we’re excited to keep improving our practice in 2026.