Building AI-Powered Apps in 2024

Over the past year, I've shipped several AI-powered applications including CareerWeave AI and InstaDeck. Along the way, I've accumulated a collection of hard-won lessons that I wish someone had told me when I started. This isn't a tutorial - it's the real talk about what actually happens when you put AI into production.

Starting With the Right Question

When I first got access to the OpenAI API, I fell into the same trap as everyone else. I started thinking about all the cool things AI could do. What a mistake. The first AI feature I built was technically impressive and completely useless. Users didn't care that I could generate text - they cared about solving their problems.

Now, before writing any code, I ask one question: "What problem am I solving, and how does AI solve it better than existing solutions?" For CareerWeave AI, the answer was clear. Writing tailored resumes and cover letters is time-consuming and tedious. AI can do it in seconds while maintaining personalization. That's a real value proposition.

The key word there is "better." AI for AI's sake is a recipe for a demo that impresses nobody. AI that genuinely improves someone's workflow is a product people will pay for.

Choosing Your Model Wisely

Not all AI models are created equal, and the "best" model often isn't the right choice. For CareerWeave AI, I spent weeks testing different options before landing on Gemini 2.5 Pro. The decision wasn't about raw capability - GPT-4 and Claude are both excellent. It came down to three practical factors.

First, structured content generation. Resumes and cover letters need to follow specific formats. Gemini was consistently better at respecting formatting constraints without drifting off-topic.

Second, price-to-performance ratio. When you're generating multiple documents per user session, API costs add up fast. Gemini offered the best balance between quality and cost for my specific use case.

Third, response latency. Users don't want to wait 30 seconds for their resume. Gemini's response times were consistently fast enough to feel "real-time."

The lesson here is to test models against your actual use case, not benchmarks. A model that excels at coding might struggle with creative writing. A model that's great for chat might be terrible at structured outputs.

The Error Handling Nobody Talks About

Here's what the tutorials don't tell you: AI APIs fail. A lot. Not catastrophically - they don't return errors. They return... weird stuff. Hallucinations that slip through your validation. Responses that are technically correct but contextually wrong. Formatting that's 90% right but breaks your parser on that 10%.

Building robust AI applications means building robust error handling. Every API call gets wrapped in retry logic with exponential backoff. If the first attempt fails, wait 1 second and try again. Then 2 seconds. Then 4. This handles transient network issues without hammering the API.

But retries only solve one problem. You also need fallback responses. When AI fails to generate something usable, what does the user see? An error message? A default template? The answer depends on your product, but you need an answer before you ship.

Most importantly, you need clear feedback during loading states. AI responses can take 5-10 seconds. That's an eternity in web time. Users need to know something is happening. I use streaming responses wherever possible so users see the content appearing word by word. It feels faster even when it isn't.

The Art of Prompt Engineering

I used to think prompt engineering was overrated. Just tell the AI what you want, right? Then I spent three days trying to get consistent resume formatting and changed my mind entirely.

The prompts in CareerWeave AI went through dozens of iterations. Small changes in wording led to dramatically different outputs. Adding specific examples helped. Being explicit about what NOT to do helped more. Eventually, I developed a mental model for writing prompts that I still use today.

Start with context. Tell the AI who it is and what it's doing. "You are a professional resume writer helping a job seeker create a tailored resume for a specific position." This framing dramatically improves output quality.

Then add constraints. "The resume must be exactly one page. Use bullet points for achievements. Start each bullet with an action verb." The more specific your constraints, the more consistent your outputs.

Finally, include examples. Show the AI what good output looks like. This is especially important for formatting. A single example is worth a hundred words of explanation.

Streaming Changes Everything

The first version of CareerWeave AI waited for the full AI response before showing anything to the user. Response times averaged 8 seconds. Users would click "Generate" and stare at a loading spinner, wondering if anything was happening.

Switching to streaming responses transformed the experience. Now users see their resume appearing in real-time, word by word. The actual generation time is identical, but the perceived wait is dramatically shorter. Users watch their content appear and feel engaged rather than frustrated.

Implementing streaming is more complex than simple request-response patterns, but it's worth every extra line of code for user-facing AI features.

Cache Strategically

AI API calls are expensive. At scale, every unnecessary call eats into your margins. Caching is your friend, but caching AI responses requires thought.

For CareerWeave AI, I cache at the job description level. If someone generates a resume for a specific job posting, and another user applies for the same job, I can reuse parts of the analysis. The personalization layer still runs fresh, but the job analysis comes from cache.

This cut my API costs by roughly 40% without any impact on output quality. The key is identifying which parts of your AI pipeline are deterministic (same input = same output) versus which parts need to be dynamic.

Monitoring Costs From Day One

I learned this lesson the hard way with InstaDeck. AI costs can spiral out of control faster than you realize. One poorly optimized prompt, one retry loop gone wrong, and suddenly you're looking at a $500 bill for a side project.

Set up cost monitoring from day one. I use simple alerting: if daily costs exceed a threshold, I get a notification. This has saved me multiple times from runaway processes that would have drained my credits overnight.

The Bigger Picture

Building AI-powered apps in 2024 is simultaneously easier and harder than it's ever been. The APIs are incredible - you can add genuinely intelligent features to your product with a few lines of code. But the edge cases, the error handling, the cost management, the prompt engineering - these are the details that separate demos from products.

The AI layer of your application should feel invisible to users. They shouldn't be thinking about the AI. They should be thinking about their problem getting solved. When you achieve that invisibility, you've built something worth using.