I’ve always been fascinated by the art of problem-solving. The process of taking a complex issue, breaking it down, and mapping it into the realm of programming is intellectually rewarding. However, the joy often fades when it comes to the implementation phase. This is where the limitations of programming languages and frameworks rear their heads, turning a straightforward task into a series of complications.
For instance, let’s talk about caching database queries in TypeScript. You’d think it would be simple: create a map and use a tuple (query, params) as the key. But TypeScript, constrained by its JavaScript roots, only allows strings or numbers as map keys. So, you end up doing something like this:
const cache = new Map<string, any>();
function cacheQuery(query: string, params: any[]): any {
const key = query + params.join(',');
if (cache.has(key)) {
return cache.get(key);
}
// Execute query and store result
// ...
cache.set(key, result);
return result;
}
This seems straightforward, but it’s not without its pitfalls. For example, if a parameter contains a comma, you end up with ambiguous keys that could lead to incorrect cache retrievals. To work around this, you might introduce an encoding step:
function encode(str: string): string {
return str.replace(/,/g, '\\,');
}
function cacheQuery(query: string, params: any[]): any {
const encodedParams = params.map(param => encode(param.toString()));
const key = query + encodedParams.join(',');
// ...
}
Now, you’ve added complexity to a problem that shouldn’t have been complex to begin with. You’re spending more time managing the cache key than focusing on the actual database query. It’s these kinds of issues that led me to dream of a future where the mundane aspects of coding could be automated. And that future is closer than you might think, thanks to what I’ve come to call “prompt-based software development.”
If you’ve ever wished for a Sancho Panza to handle the nitty-gritty details while you focus on the grand vision, you’re not alone. This dream is becoming a reality, and this narrative series will take you through a fictional yet plausible history of this groundbreaking approach.
In a world before prompt-based development, Marco and Ayse struggle with a legacy codebase teeming with technical debt. Their attempts to refactor lead to unforeseen challenges, setting the stage for the need for a new approach. How I Learned To Stop Worrying And Love Technical Debt.
Marco, a newcomer, faces the recurring nightmare of forgotten fixes. His experience serves as a catalyst for the team to explore more efficient ways of debugging, paving the way for prompt-based solutions. Debugging Déjà Vu: A Story of Forgotten Fixes.
Inspired by the chaos of her son’s room, Ayse conceptualizes the idea of automated code refactoring through prompts. This marks the first major breakthrough in prompt-based development. Code that Cleans Itself: A Journey into Automated Refactoring.
Marco introduces “Proglogging,” a structured, prompt-based approach to problem-solving. This innovative method becomes a cornerstone of the team’s new development paradigm. Proglogging: The Developer’s Detective Toolkit.
The team integrates AI into their prompt-based development process. Despite initial skepticism, they come to see AI as a new “team member” capable of generating and responding to prompts. When AI Joins the Dev Team.
I reflect as an engineering manager on the team’s journey and the ethical considerations of AI in prompt-based development. My insights offer a glimpse into the future of this evolving field. Pair-Programming with AI: A Tale of Man and Machine.
Whether you’re a developer, a tech manager, or just someone intrigued by the future of technology, this series offers a comprehensive look into a world where human ingenuity meets artificial intelligence through the lens of prompt-based development. It’s a tale of innovation, teamwork, and the quest for efficiency.