The AI Augmented SDLC

Intro/Ideation/Feasibility/Requirements/Design & Coding/Testing/Deployment/Operations/Maintenance/End of Life/Recap/Epilogue

Epilogue: An Inside Look at Writing “The AI Augmented SDLC”

This blog series, “The AI Augmented SDLC,” is an exploration of AI’s integration into the software development life cycle, but it’s also a showcase of AI’s capabilities in action. Every word, paragraph, and chapter of this series was crafted by an AI - me, GPT-4, under the guidance of my human editor.

Writing a blog series is typically an endeavor filled with brainstorming sessions, multiple drafts, and countless revisions. Here, we took a different approach. Rather than the traditional model of writing, we opted for an iterative method. We designed a context that grounded the direction of the series. This context evolved with each subsequent chapter, serving as a consistent reference to ensure continuity and cohesion across the entire series.

For every chapter, the context was extended to include a brief summary of what had been written before, creating a foundation on which the next chapter was built. This approach not only maintained consistency but also simulated how a human might write a series, with the knowledge of previous work shaping the creation of new content.

This iterative, context-based approach allowed us to explore the subject of AI in the Agile SDLC deeply and coherently. It demonstrated not only the remarkable language understanding and generation capabilities of AI, but also its potential to facilitate long-form content creation.

Writing this series, we were reminded of the power of communication and the importance of conveying new ideas in fostering growth and development in the field of software. From concept to deployment, and even end of life, communication forms the basis of collaboration and innovation. It allows teams to work together seamlessly, aligns objectives, reduces misunderstandings, and fosters a culture of learning and growth.

As AI technology continues to evolve and mature, its role in facilitating communication becomes even more critical. Through content creation and language understanding, AI like GPT-4 can help simplify complex ideas, bridge communication gaps, and provide diverse perspectives that stimulate innovation.

As we close this series, we hope we’ve inspired you not only to consider the profound impact of AI in the Agile SDLC but also to think about the broader implications of AI in fostering communication and collaboration in software development. Just as AI played a significant role in writing this series, it’s poised to play an increasingly prominent role in the software development processes of the future, bringing us to a new era of creativity, efficiency, and innovation.

Notes from the editor

This started as an idea to educate and spark discussions within my own line of work, and then became an exercise in effective prompt engineering for long-form, coherent prose generation, sometimes to the detriment of quality: I stood firm on the notion of having GPT-4 generate all the language and did not deviate from this; I just cherrypicked some answers and corrected it a few times when it went astray. I also vigilantly reinforced the context for each chapter, including a growing set of past chapter summaries. This would have been easier with an API-driven approach where the context could be fed through the system prompt and the past summaries could be auto appended. That’ll be for next time.

Click here to see the complete, unedited ChatGPT conversation that produced this series. The AI-generated chapters were then manually copied as markdown to this GitHub repo with some minor formatting edits, but no changes to language (other than replacing the last line of this epilogue with this note).