While integrating a legacy payment provider, we ran into one of those classic “legacy fintech” hurdles of sparse documentation, slow vendor response, and enormous trial-and-error. What started as a straightforward Python-to-NodeJS port became a deep dive into cryptography, networking and the nuances of legacy software. Yes, it was frustrating but it was also a chance to lean on first-principles thinking and solid engineering fundamentals.
You might wonder why we compare engineering by-products to industrial waste. In traditional manufacturing, “waste” refers to materials left over from the main production process, things like offcuts, scrap, or spent grain. While once considered useless, many companies discovered these leftovers could be transformed into valuable new products or even entire business lines.
In engineering, we produce similar “waste”, debug logs, code samples, early drafts, design notes, Slack threads, side conversations, or even technical rants. These are often overlooked or discarded once the main task is done. But just like in industry, when we intentionally capture and re-use this output, we discover hidden value. These by-products, repurposed with the help of AI and good documentation habits, become the raw material for learning, content, and growth.
In other words, what might look like clutter in the moment can become a source of strategic advantage if we treat it with the right mindset. In fact, this very blog post is a by-product of an engineering task. With a bit of organizational discipline and a few ChatGPT threads, we were able to quickly assemble a draft. That initial draft; born from real engineering work, then flowed into our regular content pipeline for review and refinement.
By capturing raw engineering knowledge in structured or semi-structured formats (including Slack threads or ChatGPT conversations), we can leverage AI to remix, summarize, or transform this “waste” into high-value resources.
This directly supports the Single Source Principle: a single, well-maintained source of truth; whether it’s a doc, code comment, or debug artifact to power multiple outputs like blog posts, knowledge base entries, customer docs, or LinkedIn campaigns.
Ultimately, this is about staying consistent with Prodigal’s vision of being an AI-first engineering organization.
Instead of relying only on manually curated docs or top-down knowledge transfer, we’re experimenting with making everyday engineering output fuel for AI-driven content generation, onboarding, and even marketing.
As we scale, this approach gives us leverage both internally (for onboarding, retros, and culture) and externally (for branding, DevRel, and sales). Previously, we saw this approach work for our Apps team’s RCA (Root Cause Analysis) process; focused on creating better RCA docs for product learning and incident reduction.
The goal is to make it easy for us and our AI tools to learn from everything we do, not just fix issues. Over time, this builds a smarter, more connected internal knowledge base. It’s no wonder some developers are drawn to companies like Meta or Google, not just for the scale, but for the depth of internal knowledge (research, decks, case studies, community notes) that’s simply not available elsewhere.
Treating the by-products of engineering isn’t only efficient but it’s strategic as well. When we actively capture and refine these knowledge outputs, we unlock new leverage through AI to build a self-improving internal knowledge base that strengthens our engineering culture.
The result? Faster onboarding, smarter retrospectives, better documentation, and content that amplifies our brand.
At Prodigal, we believe this AI-first, knowledge-driven mindset is what will differentiate the next generation of engineering organizations. By rethinking what we do with our “industrial waste,” we’re not only solving legacy problems but we’re paving the way for a more adaptive and innovative future.
Let’s keep building, learning, and turning our everyday work into lasting advantage!
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