Building an AI-based web application
When I first set out to build an AI-driven app from scratch, I thought the hardest parts would be the usual: fiddling with model parameters, figuring out the right APIs, making sure the code didn't collapse under its own weight. Oh boy, was I wrong. The real hurdles turned out to be everything around the code-the process, the decisions I made too early (or too late), and the trade-offs that only revealed themselves when things broke.
The whole experience was messy, enlightening, and at times pretty frustrating. But it also forced me to think differently about building AI systems (or software in general). This is me trying to capture that experience for other developers, in case you ever find yourself going down the same rabbit holes. What started as a straightforward AI project turned into a masterclass in product development, technical debt, and the hidden complexities that emerge when you're trying to build something real that real people will actually use. Looking back, I realize that most of the pain points weren't technical in the traditional sense-they were systemic problems about choosing the wrong abstraction levels and fundamentally misunderstanding what "production-ready" actually means.
The MVP Trap (aka Streamlit Overstretched)
I started, like many do, with Streamlit. It's quick, forgiving, and lets you move from idea to prototype in a matter of hours. For an MVP, it's unbeatable. I could throw in logic, test user flows, and even spin up something shareable without much ceremony. The appeal is immediate: write Python, add some decorators, and suddenly there's a web app. No frontend/backend separation, no REST API design, no deployment headaches. Just pure functionality wrapped in a decent-looking interface. When I discovered this, Streamlit felt like magic. I could iterate on features in real-time, show stakeholders working demos within hours of having an idea, and validate concepts without getting bogged down in implementation details. The feedback loop was incredible. Change a line of code, refresh the page, boom -> new feature. It was exactly what I needed for those crucial first few months when I was still figuring out what I was actually building.
But here's the trap: it feels too comfortable. Before I knew it, I was duct-taping features onto a tool that was never meant to hold them. What started as a simple data visualization became a complex workflow management system. I added file uploads, user sessions, complex state management, background processing, and multi-page navigation. Each addition felt reasonable in isolation, but collectively they were pushing Streamlit far beyond its design limits.
That's what happened. Streamlit became my home for far too long. Six, seven months in, I was still leaning on it, patching around limitations, trying to make it look "production-ready." I was writing custom CSS to override Streamlit's styling, implementing hacky session state management before they had proper support for it, and creating elaborate workarounds for things that should have been simple. The cracks showed everywhere-performance issues that cropped up with more than a handful of concurrent users, stability problems when users did unexpected things like refreshing pages mid-process, and the ugly truth that I was forcing a prototype framework into production territory.
The performance issues were particularly painful. Streamlit's reactive model means that every user interaction can potentially trigger a full page re-run. This is fine for simple apps, but when making API calls to large language models, processing files, or managing complex state, those re-runs become expensive. I found myself adding increasingly elaborate caching strategies and trying to minimize the computation in each run, but I was fighting against the framework rather than working with it.
The user experience problems were even worse. Streamlit apps feel like demos, and no amount of custom CSS can completely hide that. The loading states are basic, error handling is limited, and the overall interaction model feels clunky compared to modern web applications. Users would click something and wait, not knowing if the app was processing their request or had frozen. The lack of proper loading indicators and progress bars made even fast operations feel slow.
In hindsight, I should have cut ties earlier. The signs were all there: I was spending more time fighting Streamlit's constraints than building features, the codebase was becoming a maze of workarounds, and user feedback consistently mentioned that the app felt "rough around the edges." But the migration seemed daunting. I had months of work tied up in this Streamlit app, and the thought of rewriting everything in a proper web framework felt like a massive step backward.
The lesson is simple: Streamlit is brilliant for getting something out fast, but it's not a long-term solution for anything complex. It's a prototyping tool, and like all prototyping tools, it becomes a liability when you try to turn the prototype into the product. If you see even a glimmer of traction, migrate. Next.js (in my case) gave me the stability, scalability, and control I needed. The transition was painful-I basically had to rewrite the entire frontend-but the result was night and day. Suddenly I could implement proper loading states, handle errors gracefully, and create the kind of polished user experience that users deserved.
If I were to do it again, I'd draw a hard line: Streamlit until MVP feedback is in, then migrate the minute I see it working. Don't wait for the pain to become unbearable. Plan the migration from day one, and treat Streamlit as temporary scaffolding, not the foundation of the application. The sooner you acknowledge that your prototype needs to be rebuilt, the easier the transition will be.
(If I could draw a diagram here, it would be a cliff curve: usability skyrockets early with Streamlit, but stability plummets if you try to keep climbing on it. There's a sweet spot somewhere around month three where you should jump to a proper web framework, but most people miss it because it's too comfortable to leave what you have.)
Architecture: Keep It Boring
I'll admit it: I over-engineered. The hype around "agentic RAG" got the better of me. Instead of asking "what's the simplest way to solve this problem?", I found myself building elaborate workflows: retrieve, analyze, re-retrieve, synthesize, format. I read papers on multi-step reasoning, watched impressive demos of agents that could "think" through complex queries, and thought, "This is what users need." So instead of a straightforward RAG flow-retrieve relevant chunks, stuff them in a prompt, get a response-I created an elaborate system where different reasoning steps handled different aspects: initial retrieval, gap analysis, targeted re-retrieval, synthesis, and final formatting.
It looked sophisticated in architecture diagrams. Demo presentations showed the system "reasoning" through complex bid requirements, breaking them down, and systematically gathering information from multiple sources. Stakeholders loved watching it work through a query step-by-step. But debugging was a nightmare. When responses were wrong-and they frequently were-I had to trace through multiple reasoning steps, each with their own retrieval calls, prompt templates, and failure modes. A simple retrieval failure became a cascade of confused reasoning. What should have been one retrieval + generation became four or five sequential LLM calls, turning 10-second responses into 2-minute waits.
The breaking point came during user testing. A user submitted a straightforward bid question, and my elaborate reasoning system took over a minute to respond with an answer that was demonstrably worse than a single well-prompted GPT-4 call with simple RAG. The multi-step reasoning had introduced hallucinations in the analysis phase and lost important context during re-retrieval. Meanwhile, I tested the same question with basic retrieval + generation and got a perfect response in under 15 seconds.
That's when I realized my mistake: I was optimizing for architectural sophistication rather than user value. The agentic reasoning system was intellectually satisfying to build, but it was solving a problem that didn't exist. My users didn't care about watching the system "think"-they cared about getting accurate, relevant responses quickly and reliably.
For production, boring is good. Simple retrieval, well-crafted prompts, and direct generation will keep the system stable and debugging sane. I now use agentic frameworks (LangGraph, AutoGen, etc.) for demos or exploratory POCs, but strip them away when I hit production where things actually need to work. I can always add reasoning complexity later when there's a clear need for it and proven retrieval quality.
The Data Mess
This one hurt. I underestimated how unprepared the source documents were for RAG. I assumed that once we had access to the document repositories, plugging them into our retrieval system would be straightforward. Wrong again. The documents were inconsistent, outdated, and in many cases, missing critical context. We lost three months chasing "what if" document sources, rewriting retrieval strategies mid-build, and cleaning content on the fly. It was a humbling lesson in the difference between "having documents" and "having retrievable knowledge."
The initial promise was exciting. We had access to what seemed like a treasure trove of information: years of bid documents, proposal templates, compliance guidelines, and technical specifications from multiple departments. On paper, it looked like everything we needed to build a comprehensive RAG system that could provide rich, contextual responses for bid writing. The document owners assured us that everything was "well-organized and up-to-date," and early samples looked promising.
Reality hit hard when we started working with the full document corpus. What looked clean in small samples was actually a nightmare at scale. Document formats were inconsistent-some PDFs had extractable text, others were scanned images that required OCR. Word documents contained embedded objects, complex formatting, and version control artifacts that interfered with retrieval. Technical specifications were spread across multiple files with inconsistent naming conventions and no clear hierarchy.
The document metadata was out of date, sometimes by years. Documents that were marked as "current" were actually superseded by newer versions stored elsewhere. Version numbers were inconsistent-some used semantic versioning, others had arbitrary numbering schemes, and many had no version tracking at all. Compliance requirements that should have been standardized had dozens of variations with slight wording differences that completely changed their legal meaning. We spent weeks just understanding which documents were actually authoritative versus which were drafts, templates, or outdated copies.
But the real killer was the context fragmentation problem. Just as we'd get our retrieval working well on one document type, we'd discover that the answers required cross-referencing information scattered across different document repositories. "Oh, you need the technical requirements? Those are in the engineering specs." "The compliance details? Those are in the regulatory database." "Historical bid outcomes? We track those in the CRM, but in a completely different format."
Each new document source meant understanding different chunking strategies, different metadata schemas, and figuring out how to maintain coherent context across retrievals. Sometimes a single user question required information from multiple document types, which meant either retrieving massive amounts of content or implementing complex multi-step retrieval chains. The retrieval scores would be high for individual chunks, but the semantic coherence across different sources was terrible.
We fell into the trap of thinking we needed comprehensive document coverage. Every new document repository looked like it could unlock additional context for our responses. Why settle for basic bid writing when we could provide rich, cross-referenced insights from every possible source? This led to increasingly complex ingestion pipelines that tried to chunk and index everything into a unified knowledge base. The processing time grew exponentially, and the retrieval quality actually got worse. Any change in any document would trigger re-indexing cascades that could break retrieval for hours.
The smarter way? Define your minimum viable knowledge base upfront. What's the absolute baseline document set you need for useful responses? Lock that in, clean it early, and get your retrieval working reliably on just that core content. This isn't just about identifying which documents to include-it's about understanding retrieval quality, chunking strategies, embedding drift, and what happens when documents get updated. Everything else-nice-to-have document types, extended context-can come later. If you don't do this, you'll burn months fighting retrieval quality issues that could have been avoided.
We should have started with the smallest possible document set that could deliver user value, gotten retrieval working reliably on that, and then incrementally added document types while monitoring quality metrics. Instead, we tried to index everything, and the result was a system where retrieval was unpredictable, responses were inconsistent, and debugging failed generations required tracing through hundreds of irrelevant chunks. The irony is that when we finally simplified and focused on just the core documents users actually referenced, the response quality improved dramatically. Sometimes less really is more.
Scaling Pains: FastAPI, Polling, and Redis Epiphanies
Our first FastAPI app worked fine in controlled tests. But once we layered in heavy RAG workflows (retrieval + LLM calls for every request), it started choking. A single bid analysis would trigger vector searches, multiple document retrievals, embedding computations, and LLM generation calls. We were polling for long-running analysis status, and background document processing was colliding with live RAG requests. In demos with single users, it looked fine; under real use with multiple concurrent requests, it buckled. This was my crash course in the difference between "works with test queries" and "works in production with real document processing loads."
The warning signs were there early, but I misinterpreted them. During development, I'd test features one at a time, wait for each request to complete before trying the next one, and generally use the app in the most patient, methodical way possible. Everything seemed fast and responsive. The LLM calls took a few seconds, the data processing was snappy, and the user interface felt smooth. I was lulled into thinking we had a performant system.
The reality of user behavior was very different. Users don't wait politely for one analysis to finish before starting another. They'd submit multiple bid questions simultaneously, switch between different document sets, refresh pages when RAG responses seemed slow. When we started beta testing with real users, the system collapsed immediately. Multiple concurrent retrieval requests would overwhelm our vector database. Long-running document processing would starve the embedding API of resources. Users would get impatient with 30-second RAG responses and refresh, triggering duplicate expensive retrieval + generation chains.
The architecture was fundamentally flawed for concurrent RAG workloads. Each API endpoint was trying to do the full retrieval-generation pipeline synchronously. A single request might trigger vector searches across multiple indices, retrieve and rank dozens of document chunks, make embedding API calls, and generate LLM responses-all in the main request thread. With one user asking simple questions, this worked fine. With three users running complex bid analyses simultaneously, the server would become unresponsive. The vector database would queue up similarity searches, but there was no intelligent prioritization or resource management for expensive operations.
The polling problem made everything worse. Because RAG operations could take 20-60 seconds for complex queries, we implemented a polling-based status system where the frontend would repeatedly check if a retrieval-generation job was finished. This seemed reasonable in isolation, but it created a multiplicative effect on server load. Each user might have 2-3 active RAG requests polling for status every few seconds, which meant our API was handling dozens of lightweight status checks per minute on top of the expensive retrieval and generation work.
Background document processing was the final straw. We had document ingestion jobs, embedding updates, index maintenance, and cleanup tasks running on the same server as the user-facing RAG API. During peak usage, these background processes would compete with live retrieval requests for vector database connections and embedding API quota. The result was unpredictable performance where simple queries might be fast at midnight but take minutes during business hours when document processing was running.
This was the point I discovered the beauty of server-sent events (SSE), Redis caching, and Celery workers-not through careful planning, but through desperation. SSE solved the polling problem by letting the server push RAG progress updates to the client instead of the client constantly asking "Is my retrieval done yet?" This eliminated 70% of our API traffic overnight. Redis caching helped us avoid repeated expensive retrieval operations by storing results for common queries and frequently accessed document chunks. But the real game-changer was Celery.
Offloading RAG workflows to background workers kept the main app responsive. Instead of trying to handle vector searches, document retrieval, and LLM generation in the web request thread, we'd queue these operations and return immediately with a task ID. The user would get instant feedback that their bid analysis was being processed, and they could see real-time updates via SSE as the background workers progressed through retrieval, context assembly, and generation phases. This architectural change made the app feel orders of magnitude faster, even when the underlying RAG operations took the same amount of time.
Cutting down polling reduced wasted network chatter and server load. Redis gave us breathing room by caching repetitive results and providing fast session storage. But more importantly, these changes forced us to think differently about user experience. Instead of making users wait for operations to complete, we could show progress, provide intermediate results, and let users continue working while computations ran in the background.
The monitoring and debugging improvements were equally important. With everything happening synchronously in the main thread, it was hard to understand where bottlenecks were occurring. With background tasks, we could monitor queue lengths, worker performance, and task completion rates. This gave us much better visibility into system health and performance characteristics.
These sound like textbook lessons, but the difference is real when you live through it: an app that feels "fine" with a single user can collapse instantly when five people hammer it in parallel. The scaling challenges weren't just technical-they were also about understanding user behavior, managing expectations, and designing for real-world usage patterns rather than idealized test scenarios.
Scalability isn't something you bolt on-it's in the design choices you make upfront. Asynchronous processing, proper caching strategies, background job queues, and real-time communication patterns need to be architectural decisions, not afterthoughts. The sooner you accept that users will stress your system in ways you never imagined, the sooner you can build something that actually works when it matters.
Overbuilding Processes (Hello, Linear)
Developers like structure. But too much structure too early can kill momentum. I learned this the hard way with Linear. Before we even had a Phase-1 launch, we were drowning in grooming sessions, ticketing, and burndown charts. All while the product itself was still wobbly. It was a classic case of optimizing the process before understanding the problem we were actually trying to solve.
The appeal of sophisticated project management tools is seductive, especially when you're coming from a world of ad-hoc development and informal communication. Linear looked beautiful, with its clean interface, powerful filtering, and impressive automation capabilities. The idea of having every task tracked, every requirement documented, and every sprint planned seemed like the professional thing to do. After all, successful companies use proper project management, right?
So we dove in headfirst. We created detailed user stories for features that were still theoretical. We estimated story points for work that we didn't yet understand. We set up elaborate workflows with multiple states, approval processes, and automatic transitions. We scheduled regular grooming sessions where we'd spend hours debating the priority of tasks that might not even be relevant by the time we got to them.
The grooming sessions became particularly absurd. We'd sit in a room for two hours, dissecting a ticket about improving the user onboarding flow, when we weren't even sure what the core product workflow should be. We'd debate whether something was a 3-point or 5-point story, when we had no historical data to calibrate our estimates against. We'd argue about acceptance criteria for features that no user had ever requested, based on assumptions about user behavior that we'd never validated.
Instead of helping, it slowed us down. We were spending 20-30% of our development time on process overhead: writing tickets, updating status, attending meetings about meetings. We were managing tickets about features that didn't even exist yet, obsessing over workflows instead of validating the core product. The overhead became self-perpetuating-we needed more process to manage the complexity that the existing process had created.
The real problem was that we were applying enterprise-scale project management to a startup-scale product development challenge. Linear and similar tools are designed for teams that have established products, well-understood requirements, and predictable development cycles. They're built for optimization, not exploration. When you're still figuring out what to build, elaborate process management is not just wasteful-it's counterproductive.
The tickets became a source of false confidence. Having everything written down and categorized made it feel like we had clarity and control, when in reality we were just documenting our assumptions and calling them requirements. We'd point to our well-organized backlog as evidence that we were making progress, even when we weren't actually building anything that users wanted.
The burndown charts were particularly misleading. We'd complete story after story, watch the burndown chart slope downward, and feel productive. But many of those completed stories were for features that we later realized were unnecessary, or implemented in ways that didn't actually solve user problems. We were optimizing for velocity instead of value, measuring output instead of outcome.
It was classic premature optimization-this time in project management. We were trying to solve problems we didn't have yet while ignoring the problems we actually did have. Instead of focusing on user feedback, product-market fit, and rapid iteration, we were focused on process compliance and metric optimization.
The wake-up call came during a particularly intense grooming session where we spent 45 minutes debating the priority of a feature request that, upon closer examination, was based on feedback from a single user who hadn't used the product in weeks. Meanwhile, we had a backlog of urgent bug reports and performance issues that we kept pushing to the next sprint because they weren't properly "groomed" yet.
My conclusion: keep it light until the app is stable. A simple backlog and a few clear tasks are enough for early-stage development. Use whatever lightweight tool helps you stay organized-a shared document, a simple Kanban board, or even just a prioritized list. The goal is to track what needs to be done, not to create an elaborate system for managing complexity that doesn't yet exist.
Bring in Linear or Jira only when you actually have a product that needs that level of reporting and process management. When you have multiple teams, complex dependencies, and well-understood requirements, then the overhead of sophisticated project management starts to pay dividends. But in the early days, when you're still figuring out what to build and how to build it, heavy process is more likely to slow you down than speed you up.
The most productive periods of our development were when we ignored the ticketing system entirely and just focused on solving user problems. We'd pick the most important thing, work on it until it was done, then pick the next most important thing. Simple, fast, and effective. The elaborate process came later, when we had enough stability and scale to justify the overhead.
Users, Feedback, and the Waiting Game
Here's something I didn't expect: users don't always know what they want until they see it working with their actual documents. We'd put a prototype in front of them, and suddenly preferences appeared out of nowhere-chat interface vs structured forms, different retrieval strategies for different document types, completely different workflow patterns we'd never considered. The MVP wasn't just for us to test RAG feasibility; it was a mirror for users to discover how they actually wanted to interact with their knowledge base. This was both enlightening and frustrating in equal measure.
In the early requirements gathering phase, users would give us what seemed like clear, actionable feedback. "We need a system that can find relevant information from our bid documents and help us write responses." Straightforward enough. We'd build exactly that, only to discover during demos that what they really meant was something completely different. They wanted to trace back to source documents, not just get generated text. They wanted to compare requirements across multiple bids, not just analyze one at a time. They wanted to export structured data for their existing workflows, so the output format mattered as much as the content quality itself.
The problem is that users can't easily articulate needs for RAG workflows they've never experienced. It's like asking someone to describe their ideal research assistant when they've only ever used search engines. They'll tell you they want better search, not a conversational interface that can reason across documents. Only when they see a working RAG system do the real requirements emerge. "Oh, but how do I know if this information is current?" "Can I see which documents this came from?" "What if I need to verify this with the original compliance language?"
This led to some frustrating cycles where we'd build exactly what was requested, only to have users say "This is great, but..." followed by a list of changes that essentially meant rebuilding the entire retrieval and presentation layer. Not because we'd implemented RAG wrong, but because seeing the working system helped them understand what they actually needed from AI-powered document interaction. The feedback wasn't criticism of our implementation; it was users discovering the gap between "getting answers" and "getting trustworthy, actionable answers."
That's the fun bit-watching users discover possibilities they hadn't imagined. We'd show them basic bid requirement extraction, and they'd immediately start brainstorming ways to apply it to contract analysis, compliance checking, or competitive intelligence. Or they'd see our document comparison feature and realize they could use it to identify inconsistencies across proposal templates in ways they'd never considered. These moments of discovery were incredibly valuable, but they also meant constant scope creep as every demo turned into a brainstorming session about new use cases.
The painful bit? Getting feedback in the first place. It could take weeks-calls delayed because of competing priorities, test sessions canceled at the last minute, feedback drip-fed over months in tiny increments. Users are busy people with their own deadlines and pressures. Participating in product development feels like a nice-to-have activity that gets pushed to the bottom of their to-do list whenever anything urgent comes up.
The feedback collection process itself became a bottleneck. We'd schedule a demo, spend hours preparing the perfect presentation, and then have it canceled at the last minute because the user had a client emergency. When we finally did get time with users, they'd often be distracted, rushing through the session, or trying to multitask. Getting thoughtful, actionable feedback required patience and persistence.
Even when we got feedback, it was often inconsistent across different users. User A would love the chat interface, while User B would hate it and prefer a form-based approach. User C would want everything automated, while User D wanted manual control over every step. Reconciling these conflicting preferences into a coherent product vision was like solving a puzzle where half the pieces kept changing shape.
As a developer, sitting in that limbo is brutal. You can't move forward without their input, but you can't just stop either. There's always the temptation to make assumptions and keep building, but that usually leads to rework when the feedback finally comes in. At the same time, sitting idle while waiting for feedback feels like wasted time, especially when you're under pressure to deliver.
The problem compounds over time. Early in the project, a two-week delay for feedback might be annoying but manageable. Later, when you're approaching deadlines and trying to polish the final product, that same delay can derail your entire timeline. Users don't always understand that their feedback delays have cascading effects on development schedules.
My advice: set expectations upfront, and be ruthlessly clear about them. Make it clear that feedback delays equal delivery delays. Build buffer time into your schedules for feedback cycles, and communicate the impact of delays clearly and frequently. If a user can't provide feedback by a certain date, they need to understand that it will push back the delivery timeline by a corresponding amount.
Otherwise, you end up crunching in the last month because "waiting time" never made it into the timeline. Project managers will look at the development estimates and assume that's the total timeline, not realizing that the feedback and iteration cycles can easily double the actual time to completion. The solution is to make these dependencies visible and plan for them explicitly.
The most successful feedback cycles we had were when we structured them like regular meetings with clear agendas and deliverables. Instead of ad-hoc "whenever you have time" requests, we'd schedule recurring sessions with specific goals: "In next Tuesday's session, we'll review the new dashboard interface and make decisions about the filtering options." This gave users a framework for providing feedback and helped us keep momentum.
Deployment Surprises
Here's a rookie mistake: assuming what runs on your local server will run the same way in production. Spoiler: it won't. The first time I pushed code to the cloud, everything broke-configs, services, security assumptions. What had been smooth locally became a patchwork of rewrites. This was my introduction to the concept that "works on my machine" is not a deployment strategy.
The signs should have been obvious. My local development setup was a carefully curated environment where I had full control over versions, configurations, and services. I was running everything with elevated permissions, using localhost for all connections, and relying on services that were installed and configured exactly the way I needed them. It was a pristine, stable environment that bore no resemblance to the real world where the application would actually run.
When deployment time came, I was confident. The application worked perfectly locally, all the tests passed, and I'd even done some basic load testing with good results. I spun up a cloud instance, deployed the code, and... nothing worked. Database connections failed because of SSL requirements that didn't exist locally. API calls timed out because of network restrictions. File uploads broke because of permission issues. Environment variables weren't set correctly, and hardcoded paths that worked fine on my machine were completely wrong in the cloud environment.
The configuration differences were more extensive than I'd anticipated. The cloud environment had different Python versions, different system libraries, and different security policies. Services that started automatically on my development machine needed to be explicitly configured and managed in production. Dependencies that I'd installed months ago and forgotten about weren't available in the production environment.
Security assumptions were particularly problematic. Locally, I was running everything as a single user with broad permissions. In production, the security model was much more restrictive. The application couldn't write to certain directories, couldn't make outbound connections to arbitrary hosts, and couldn't access system resources the way it could locally. Features that worked perfectly in development simply couldn't function in a properly secured production environment.
The learning here: do a quick infrastructure scan early. Don't wait until you're ready to deploy to understand the constraints and requirements of your target environment. If you're on AWS, Azure, or GCP, figure out the quirks before you write half your code. Understand the security model, the networking requirements, the service dependencies, and the configuration management approach. It'll save you weeks of patching later.
Better yet, set up a production-like staging environment as early as possible. Deploy your MVP there and keep it working as you add features. This forces you to think about deployment considerations throughout the development process rather than treating them as an afterthought. It also gives you confidence that your deployment process actually works before you're under pressure to ship.
Closing Thoughts
If I had to sum it all up: most of my pain came from overcomplicating architecture and underestimating the boring bits. The best moments were when we kept it simple-single API calls, minimal abstractions, quick MVPs that delivered immediate user value. The worst moments were when we fell in love with process or frameworks and forgot the actual goal: build something people want to use that actually works when they need it to.
The technology choices mattered less than I thought they would. Whether we used React or Vue, PostgreSQL or MongoDB, AWS or GCP-these decisions had much smaller impact on success than I expected. What mattered was understanding the problem we were solving, getting feedback quickly, and iterating based on real user needs rather than theoretical architectural principles.
The human factors were consistently more challenging than the technical ones. Managing stakeholder expectations, coordinating feedback cycles, balancing competing user requirements-these soft skills turned out to be just as important as coding ability. Building AI applications isn't just about understanding machine learning; it's about understanding people and how they interact with complex systems.
Would I do it all again? Absolutely. But next time, I'd keep Streamlit in its box, start with the minimum viable dataset, and remind myself that boring architectures are usually the ones that survive. I'd spend more time upfront understanding the deployment environment, set clearer expectations about feedback timelines, and resist the temptation to build elaborate systems before validating that anyone actually wants them.
Most importantly, I'd remember that the goal isn't to build impressive technology-it's to solve real problems for real people. The most elegant code in the world is worthless if it doesn't make someone's life better. The most sophisticated AI architecture is a failure if users can't figure out how to use it. The most comprehensive dataset is useless if you can't turn it into insights that people can act on.
Building an AI application taught me that success isn't about having the latest technology or the most cutting-edge architecture. It's about understanding your users, solving their problems reliably, and staying focused on delivering value rather than showcasing technical sophistication. Sometimes the boring solution really is the best solution.