Who is Tom Smykowski and Why Hire Him for Prompt Engineering?
Tom Smykowski is a Poland-based remote product engineer with nearly two decades of experience in product delivery, combining deep engineering, UX, and product thinking. He is the ideal hire for founders, CTOs, and executives seeking to build or scale AI-powered features within their products. Tom specializes in taking ownership from discovery through production, ensuring that prompt engineering and AI integrations are not just technically sound but also drive real user value and business outcomes. His work with EU, UK, and US teams makes him adept at cross-time-zone collaboration, focusing on integrating AI seamlessly into product workflows.
Prompt engineering for products goes beyond crafting clever inputs for large language models. It involves designing systematic, scalable, and testable interactions between your application and AI services to create reliable, user-centric features. This requires a blend of software architecture, product sense, and an understanding of AI capabilities and limitations. Tom's background in building products from scratch at Websoul, modernizing systems at Printify, and leading technical initiatives at Croissant and Fream provides the perfect foundation for this work. He translates complex AI potential into tangible, maintainable product features.
His approach is grounded in product engineering first. This means prompt engineering is treated as a core product capability, integrated into the development lifecycle with proper quality gates, testing automation, and user feedback loops. For teams looking to move beyond experimental AI prototypes and into production-grade features, Tom provides the technical leadership and hands-on implementation to make it happen. He ensures that AI enhances your product without introducing undue complexity or reliability issues for your engineering team or your end-users.
From Prompt Crafting to Product Systems
The journey from a simple AI demo to a robust product feature is vast. Effective prompt engineering for products requires building systems. This includes designing prompt templates, managing context windows, handling variable injection, implementing fallback strategies, and logging interactions for continuous improvement. Tom's experience in architecting scalable frontend systems at Printify for over 60 engineers and building a marketing automation platform serving 32,000+ businesses at Websoul demonstrates his ability to design systems that scale. He applies this systems thinking to AI, ensuring prompts are part of a reliable, observable, and improvable pipeline.
Consider a SaaS application adding an AI-powered content summarization feature. A naive implementation might directly call an API with a static prompt. A product-engineered approach, which Tom implements, involves a configurable prompt registry, A/B testing frameworks for different prompt versions, user role-based context tailoring, rate limiting, cost tracking, and comprehensive error handling. This transforms a clever API call into a hardened product feature. His work integrates with existing tech stacks, leveraging TypeScript, Vue.js, React, and robust backend services to create cohesive experiences.
Furthermore, this systemic view extends to the team and process. Tom advocates for and implements AI-native software development lifecycles. This includes establishing quality gates specific to AI outputs, creating automated testing suites for prompt variability, and upskilling engineering teams on best practices. His goal is to embed prompt engineering competence within your product team, moving from a reliance on a single expert to a shared, scalable capability. This is crucial for long-term product evolution and innovation.
Integrating AI with Existing Product Architecture
Adding AI features to an existing product is not a greenfield project. It requires careful integration with current architecture, data models, and user flows. Tom's extensive background in full-stack development and design systems is critical here. He evaluates how AI services like the OpenAI API can connect to your existing APIs, databases (such as Supabase), and frontend components without creating tech debt or performance bottlenecks. His work with complex data interfaces using AG Grid and mapping libraries at Fream informs his approach to handling AI-generated data within sophisticated UIs.
The integration challenge often lies in state management, data persistence, and user interaction design. For instance, an AI-assisted form-filling tool needs to manage partial generations, user corrections, and final submissions seamlessly. Tom engineers these interactions by building upon solid software principles, ensuring the AI feature feels like a natural extension of the product, not a bolted-on afterthought. His product-minded focus ensures that the integration prioritizes user experience and conversion goals, a skill honed through projects for clients like GE Healthcare, Unilever, and the Polish Tourism Organisation.
Security and compliance are also paramount, especially when dealing with user data and AI providers. Having worked on compliance and collaboration features at Croissant, a workplace platform, Tom is adept at designing architectures that respect data governance, privacy regulations, and enterprise security requirements. He helps implement the necessary safeguards, such as data sanitization before API calls and secure credential management, making AI integration safe for businesses of all scales, from startups to established corporations.
Building for Scale and Maintainability
A successful AI feature must scale with your user base and remain maintainable as underlying models and APIs evolve. Tom's track record speaks directly to this need. At Printify, he contributed to a design system and rebrand supporting a platform for 10M+ merchants, an environment where consistency and scalability are non-negotiable. He applies the same rigor to AI product features, designing abstractions for prompt management, response caching, and model versioning that allow your team to iterate quickly without breaking existing functionality.
Maintainability in prompt engineering means creating a clear separation of concerns. The business logic for constructing a prompt should be isolated from the API calling code, and both should be separate from the UI rendering logic. Tom champions patterns that make it easy to update prompts based on user feedback or model improvements, to swap AI providers if needed, and to monitor performance and costs. This often involves building internal tooling or dashboards, an area where his full-stack capabilities shine, providing teams with visibility and control over their AI features.
Scalability also pertains to cost and performance. Unoptimized prompt usage can lead to ballooning API costs and latency issues. Tom implements strategies like prompt optimization, response caching, and asynchronous processing to keep costs predictable and performance snappy. He brings a focus on developer experience (DevEx) as well, ensuring that the internal APIs and SDKs built around your AI capabilities are well-documented and easy for other engineers on your team to use, fostering broader adoption and innovation within your product.
Quality Gates and AI Testing Automation
Shipping AI features with confidence requires a new approach to quality assurance. Traditional unit tests are insufficient for evaluating the often non-deterministic outputs of language models. Tom specializes in establishing AI quality gates and testing automation frameworks tailored for products. This involves creating validation suites that test prompt templates against a range of inputs, measuring output consistency, safety, and adherence to formatting rules. His work in AI testing ensures that your product maintains a high standard of reliability even as you innovate.
These quality gates are integrated into the CI/CD pipeline, allowing for automated checks on every pull request. Tom sets up systems to run prompt variations against golden datasets, flag regressions in output quality, and even monitor for prompt injection vulnerabilities. This proactive testing culture prevents bugs from reaching production and builds trust in the AI features. His experience in setting up AI software factories and agentic engineering workflows means he can automate not just the testing, but also parts of the development and review process, significantly boosting team productivity.
Beyond automation, Tom emphasizes the human-in-the-loop for critical quality reviews. He designs workflows where ambiguous AI outputs are routed for human approval, balancing automation with necessary oversight. This is particularly important in domains requiring high accuracy or sensitivity, such as those encountered in his projects for institutions like the Institute of National Remembrance or the Marshal Office of the Silesian Voivodeship. His approach ensures that AI augments human decision-making without replacing it inappropriately.
When This is a Good Fit
Hiring Tom for prompt engineering on your product is an excellent fit if you are a founder, CTO, or product leader who needs more than just API integration. It is ideal if you require a senior engineer who can own the entire lifecycle of an AI feature, from conceptualization and prompt design to scalable architecture, testing, and production deployment. He excels in environments that value product-minded engineering, technical leadership, and a partnership approach to building. If your team is based in the EU, UK, or US and you need a remote-first collaborator who can bridge time zones and drive initiatives from concept to launch, Tom's model of engagement will align perfectly with your goals.