Building Scalable GenAI Products, Part-1: A Working Backwards Framework


 

GenAI's shift from deterministic to non-deterministic systems breaks traditional development approaches. Having worked on scaling GenAI at big-tech, I've learned that success requires working backwards from desired outcomes—starting with precise problem boundaries and data quality requirements. These first two steps determine whether your GenAI product scales or remains an impressive demo.

One of the biggest differences between existing computer systems and new GenAI systems is indeterminism—and it's breaking everything we thought we knew about building scalable products.

For decades, computers excelled at doing the same task thousands of times with identical results. They weren't necessarily smarter than humans, but they had almost no variance or bias. GenAI deliberately introduces variance through "temperature" settings to enable creativity. Zero temperature produces identical, factual outputs. Higher temperature brings creativity and individuality.

This is exactly why most GenAI products fail to scale. Having worked on scaling a GenAI product at a big-tech company, I've seen how this fundamental shift from deterministic to indeterministic systems breaks traditional scaling approaches. The only successful GenAI products at scale today are chatbots where humans can curate responses.

Working Backwards: The Key to GenAI Success

Traditional software development starts with technical capabilities and builds forward. GenAI requires the opposite approach: working backwards from your desired outcome. You must start with exactly what success looks like and work backwards to the data and constraints needed to achieve it.

Here are the first two critical steps in this working-backwards methodology:

Step 1: Define Your Problem Boundaries (Working Backwards from Constraints)

Start with what you cannot allow your system to do, then work backwards to define what it should do.

Data Availability and Relevance Ask: "What would perfect input data look like for my desired outcome?" Then work backwards to assess whether you can access, curate, and maintain that data quality.

Consider a GenAI system for generating medical reports. The template isn't just about report format—it's about ensuring that patient data, test results, and reference ranges are consistently structured and validated. You need standardized data schemas, validation rules, and quality checks that ensure the GenAI system always works with clean, reliable inputs.A medical diagnosis system working backwards would require verified clinical records, peer-reviewed research, and current guidelines—not health blogs or social media. If you can't secure this data quality, the system won't scale reliably.

Legal and Risk Boundaries Ask: "What legal risks can we absolutely not accept?" then work backwards to data sourcing strategies. Take OpenAI's situation with image generation & writing capabilities that can mimic artistic styles. While this creates incredible user engagement, it has led to significant legal challenges from content creators and publishers.Smaller firms, lacking the resources to weather such disputes, must ensure compliance with copyright and fair-use laws.

Working backwards means understanding fair use, licensing, and litigation risks before choosing training data.

Solution Appropriateness Ask: "Does the desired outcome actually need creativity and variance?" If you're working backwards from consistent, predictable outputs (invoice processing, compliance reports), GenAI might be the wrong tool entirely. Traditional deterministic systems often better serve these use cases. In this case the question to ask is "What is the task that my existing system cannot solve."

Step 2: Articulate Data Quality Requirements (Working Backwards from Outputs)

Working backwards means starting with your ideal output quality and reverse-engineering the data requirements to achieve it consistently.

Data Quality Standards Ask "What would make this output unacceptable?" then work backwards to data validation requirements. For medical reports, incorrect patient data or outdated clinical guidelines would be catastrophic. This drives requirements for verified sources, data freshness guarantees, and validation pipelines.

Template-Driven Structure Ask: "What exact structure does my output need?" then work backwards to the data required. Medical reports need consistent patient data, validated test results, and standardized reference ranges. The output template drives the input data requirements.

Cross-Disciplinary Validation Ask: "Who needs to validate that our outputs actually meet real-world needs?" then work backwards to assemble the right validation team. For medical systems, this means doctors for clinical validation, patient advocates for empathy, and compliance officers for insurance requirements. This team questions whether outputs truly serve their intended purpose, not just whether the input data is good.

The Data Strategy Foundation

Working backwards from successful GenAI products reveals that data strategy determines everything:

Curation Over Volume Successful systems work backwards from quality outputs to curated, domain-specific datasets rather than trying to scale with more data. Working backwards from reliable medical diagnoses requires verified clinical documentation, not broad internet text scraping.

Verification Requirements Working backwards from reliable outputs demands verified data sources. Medical recommendations need current, professionally validated clinical guidelines—not random web content that might include outdated or dangerous medical advice.

Maintenance Strategies Working backwards from consistent long-term performance requires ongoing data quality processes: regular audits, source monitoring, freshness checks, and bias detection. GenAI systems degrade gracefully, continuing to produce outputs even as data quality decreases.

Why This Working-Backwards Approach Matters

Traditional development approaches fail with GenAI because they start with technical capabilities and hope to find good use cases. Working backwards starts with the desired business outcome and reverse-engineers the technical requirements.

This methodology forces critical decisions upfront:

  • What outcomes are you optimizing for?
  • What constraints are non-negotiable?
  • What data quality is actually required?
  • Is GenAI the right tool for this outcome?

The Foundation for Scale

These first two steps—working backwards from problem boundaries and data quality requirements—provide the foundation for everything else. Without getting these fundamentals right, advanced techniques like prompt engineering, model fine-tuning, and evaluation frameworks won't save your project.

The organizations scaling GenAI successfully aren't those with the best technical teams—they're those with the discipline to work backwards from their desired outcomes and build the data foundations to support them reliably.

In future articles, I'll cover the subsequent steps in this working-backwards framework. But these first two steps determine whether your GenAI product becomes a scalable solution or remains an impressive demo that can't handle production workloads.

Location: Redmond, WA, USA

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