Building AI-Powered Applications: Lessons from the Trenches

Source: Dev.to

Introduction

After shipping multiple AI‑powered products at Aura Technologies, we’ve learned hard lessons about what actually works in production. This isn’t theory — it’s what we discovered by breaking things in the field.

Common Pitfalls When Shipping AI

• Demo‑first mindset – A weekend AI demo works great on the happy path, impresses stakeholders, and then collapses when shipped.
• Edge cases – Unexpected inputs break everything.
• Latency – Acceptable in demos but frustrating for real users.
• Cost – Scales dramatically with real usage.
• Hallucinations – Funny in testing, embarrassing with customers.

What we do now: Build for production from day one. Every feature is stress‑tested with adversarial inputs before any demo is shown.

Prompt Engineering as Code

We initially treated prompts as an afterthought, iterating until the output looked right. That was a mistake. Prompts are code and need the same rigor:

• Version control
• Automated testing
• Documentation
• Review processes (pull‑request workflow)

A single‑word change can improve accuracy by 20 % or break a feature entirely.

What we do now: Store prompts in the same repository as the rest of the code and require PR review for any change.

Designing for Bad User Inputs

We assumed users would figure out how to prompt our AI effectively. They didn’t. Real inputs are often:

• Vague (“make it better”)
• Missing required context
• Poorly formatted
• In the wrong language

What we do now:

• Design interfaces that anticipate bad inputs.
• Add clarifying questions.
• Provide examples and guidance to steer users toward effective interactions.

Retrieval‑Augmented Generation (RAG)

In RAG systems, the retrieval step sets the ceiling for quality. Fetching the wrong documents means even the best language model can’t help.

What we do now:

• Measure retrieval quality independently.
• Track relevance, recall, and precision metrics.
• Optimize retrieval before focusing on generation.

Latency Perception and Streaming

The difference between waiting 10 seconds for a response and seeing text appear instantly is huge for user experience, even if total time is the same.

What we do now: Stream outputs by default so users see real‑time text as it’s generated.

Caching to Reduce Costs and Latency

API costs and latency add up quickly. Effective caching solves both problems.

We cache at multiple levels:

1. Exact‑match cache – Same input → same output.
2. Semantic similarity cache – Similar inputs reuse relevant work.
3. Embedding cache – Avoid re‑embedding identical content.

One product saw a 70 % reduction in API costs after implementing proper caching.

Robust Error Handling

AI systems fail in strange ways: unexpected output formats, API timeouts, rate‑limit hits, or content‑filter triggers. Generic “An error occurred” messages are unacceptable.

What we do now:

• Graceful degradation when possible.
• Clear error messages that explain what happened and how to proceed.
• Automatic retries with exponential backoff.
• Fallback behaviors for common failure modes.

Evaluating AI Quality

Traditional software has clear pass/fail tests, but AI outputs exist on a spectrum. Two responses can both be “correct,” yet one is clearly better.

What we do now:

• Build evaluation datasets for each use case.
• Use LLM‑as‑judge for scalable evaluation.
• Track metrics over time to catch regressions.
• Conduct regular human‑evaluation sprints.

Human‑in‑the‑Loop (HITL)

Automating everything end‑to‑end is tempting but usually wrong, especially early on.

Starting with humans in the loop lets you:

• Catch errors before they reach users.
• Generate training data from corrections.
• Understand failure modes.
• Build trust with stakeholders.

Model Selection vs. System Design

We assumed that picking the “best” model (GPT‑4, Claude, Gemini, open‑source) was the key decision. In practice, other factors matter more:

• Quality of training/retrieval data
• Understanding of user needs
• Prompt engineering
• System design and error handling
• UX that guides users to successful interactions

A well‑designed system with a “worse” model often outperforms a poorly designed system with the best model.

Conclusion

The biggest lesson? You can’t learn this stuff in theory. You have to ship, see how things break, and fix them. 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.