Tools: Building AI-Powered Applications: Lessons from the Trenches

Lesson 1: The Demo-to-Production Gap is Massive ## Lesson 2: Prompt Engineering is Real Engineering ## Lesson 3: Users Don't Know How to Talk to AI ## Lesson 4: Retrieval is Usually the Bottleneck ## Lesson 5: Streaming Changes Everything ## Lesson 6: Caching is Non-Negotiable ## Lesson 7: Error Handling is a Feature ## Lesson 8: Evaluation is Harder Than Building ## Lesson 9: Start with Humans in the Loop ## Lesson 10: The Model is the Least Important Part ## The Meta-Lesson: Ship, Learn, Iterate What we learned shipping AI products at Aura Technologies Everyone's building with AI these days. Most are doing it wrong. After shipping multiple AI-powered products at Aura Technologies, we've learned some hard lessons about what actually works. This isn't theory — it's what we discovered by breaking things in production. Here's a pattern we see constantly: Someone builds an AI demo in a weekend. It works great for the happy path. They get excited, show stakeholders, everyone's impressed. Then they try to ship it. Suddenly they're dealing with: What we do now: Build for production from day one. Every feature gets stress-tested with adversarial inputs before anyone sees a demo. Early on, we treated prompts as an afterthought — something to quickly iterate on until the output looked right. That was a mistake. Prompts are code. They need: A small change to a prompt can have cascading effects on model behavior. We've seen single-word changes improve accuracy by 20% — and single-word changes break features entirely. What we do now: Prompts live in version control with the rest of our codebase. Changes go through PR review. We assumed users would figure out how to prompt our AI products effectively. They didn't. Real user inputs are: What we do now: Design for bad inputs. Add clarifying questions. Provide examples. Guide users toward effective interactions. In RAG (Retrieval-Augmented Generation) systems, the retrieval step determines the ceiling of your quality. If you fetch the wrong documents, the world's best language model can't save you. We spent months optimizing our generation step before realizing retrieval was the actual problem. What we do now: Measure retrieval quality independently. Track metrics like relevance, recall, and precision. Only then do we worry about generation. The difference between waiting 10 seconds for a response and seeing text appear instantly is enormous for user experience. Same total time, completely different perception. What we do now: Stream by default. Every AI interaction shows real-time output. API costs add up fast. So does latency. Caching solves both. We cache at multiple levels: One product saw a 70% reduction in API costs after implementing proper caching. AI systems fail in weird ways. Models return unexpected formats. APIs timeout. Rate limits hit. Content filters trigger unexpectedly. Users need to understand what happened and what to do next. "An error occurred" is not acceptable. How do you know if your AI is good? This question haunted us longer than we'd like to admit. Traditional software has clear pass/fail tests. AI outputs exist on a spectrum. Two responses can both be "correct" but one is clearly better. The temptation is to automate everything. Let the AI handle it end-to-end. No human intervention needed. This is usually wrong, at least initially. Starting with humans in the loop lets you: This one surprised us. We assumed model selection was the key decision. GPT-4 vs Claude vs Gemini vs open source — surely this is what matters most? In practice, these factors matter more: Models are increasingly commoditized. A well-designed system with a "worse" model often beats a poorly designed system with the best model. The biggest lesson? You can't learn this stuff in theory. You have to ship things, see how they break, and fix them. We've built products that failed, features we had to remove, and plenty of things we're still improving. Each failure taught us something valuable. If you're building with AI, expect to get things wrong. The goal isn't to be perfect — it's to learn faster than your competition. At Aura Technologies, we're applying these lessons to build AI products that actually work in production. If you're on a similar journey, we'd love to hear what you're learning. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse - Edge cases that break everything - Users who input things no one anticipated - Latency that's acceptable in demos but frustrating in production - Costs that seemed fine at demo scale but blow up with real usage - Hallucinations that were funny in testing but embarrassing with customers - Version control - Documentation - Review processes - Vague ("make it better") - Missing context the AI needs - Formatted weirdly - Sometimes in the wrong language - Exact match: Same input → same output - Semantic similarity: Similar inputs → reuse relevant work - Computed embeddings: Don't re-embed the same content - Graceful degradation when possible - Clear error messages that explain what happened - Automatic retries with exponential backoff - Fallback behaviors for common failure modes - Build evaluation datasets for each use case - Use LLM-as-judge for scalable evaluation - Track metrics over time to catch regressions - Regular human evaluation sprints - Catch errors before they reach users - Build training data from corrections - Understand failure modes - Build trust with stakeholders - Quality of your training/retrieval data - How well you understand user needs - Prompt engineering - System design and error handling - UX that guides users to successful interactions