Building LLM-driven applications is not like building traditional software. The rules are different, the failure modes are different, and the gap between a working AI prototype and a production-ready AI system is wider than most teams expect when they start.
I recently spent a week in Mexico City recording a two-part series for the Scaling Tech Podcast, speaking with engineers, founders, and researchers who are building real AI applications under real-world constraints. From a civil infrastructure firm processing 23 terabytes of project data to an AI-powered recruitment platform that took a year to achieve acceptable accuracy, to original research on LLM safety, the people I spoke with are learning hard lessons that every team building AI applications should hear.
Here’s what they told me.
Scaling Tech Podcast host Arin Sime interviews Global AI Professor Osvaldo Ramirez Hurtado on location in Mexico.
1. Start with Business Value, Not Technology
Before writing a single line of code, the most important question your team needs to answer is: What problem are we actually solving, and for whom?
This sounds obvious, but it’s easy to get seduced by the novelty of what LLMs can do. Global AI Professor Osvaldo Ramirez Hurtado from the Panamerican Business School was direct on this point when I spoke with him:
“Think first on the need of the business rather than about the engineer or system perspective. The technology will always be there.”
He pushed further, advocating a “Proof of Value” over a “Proof of Concept”. The distinction being that you’re not just validating that a technology can do something technically interesting, but that it delivers real, measurable value to a user or business process. If you can’t clearly state the business value, you’re building a technical demo, not a product. This framing should inform every architectural decision that follows.
Watch Professor Hurtado’s full interview: Creating Real Business Value with AI
2. Plan for Hallucinations Early
If there’s one consistent theme across every team building with LLMs today, it’s that hallucination rates in early prototypes will be higher than you want. Much higher. The question isn’t whether you’ll face the problem of hallucinations in LLMs … it’s how you design your AI evaluations loops to handle it.
Pablo Fajer, Founder of Codifin, shared a candid account of building Cody, their AI-powered recruitment agent. Cody reads CVs, identifies candidates, and matches them to job openings for enterprise clients. When they first deployed it: “The percentage of our matches would be very good, but suddenly we would get people that I would say, ‘Why are we recommending him?’ And it’s something that the AI hallucinated. When the AI reads CVs, we have right now around a 92% accuracy. But we started with a 42% accuracy.”
Going from 42% to 92% wasn’t magic, it was architecture. Pablo’s team built additional AI layers to evaluate and cross-check the outputs of their primary model. They created feedback loops. They added human reviewers at key decision points to catch what the AI gets wrong. This layered approach is a practical template for any LLM-integrated application that needs to perform reliably in production. Let AI do the high-volume first pass, with humans reviewing before anything reaches the client.
The broader lesson: design your system assuming hallucinations will occur and build your quality loops accordingly before you go to production.
Watch Pablo’s full interview: AI Engineering Talent in Mexico
3. Keep Humans in the Loop
This point follows naturally from the hallucination problem, but it goes beyond accuracy. There’s a temptation, once an AI system is working reasonably well, to remove human oversight in the interest of speed and scale. Resist it. Pablo Fajer was unequivocal on this:
“AI shouldn’t run free at all. I think that you should enable all of your developers to use it, to use Cursor, to use all of the different tools that you have, but they have to be solely responsible for the product that they give.”
This applies both to AI tools used in development and to AI agents deployed in production. Enabling your engineers to use AI coding tools aggressively is the right move. This can dramatically improve the time-to-product. But accountability for the quality of what ships stays with the human engineer. The AI accelerates; but the engineer is still responsible.
In production systems, the same principle holds: identify the highest-stakes decision points in your application and ensure there’s a human in that loop, at least until you have strong evidence that the AI can be trusted at that step with a sufficiently low error rate.
4. Add Context Filters and Guardrails Before the LLM
One of the more technically interesting conversations I had in Mexico City was with Alberto Alejandro Duarte from Paradox Systems, a company working at the intersection of renewable energy and AI. His team is developing a pre-filtering guardrail layer that sits upstream of the LLM in their architecture.
The approach is inspired by principles of biological persistence. Essentially, the tendency of stable natural systems to maintain coherence over time despite noise and interference. Their filtering layer enforces certain critical rules and context constraints before any input reaches the language model. In their testing, applying this layer significantly reduced the model’s tendency to hallucinate, even when noise was deliberately introduced to confuse it. Alberto noted, “We have tested it against different types of software that are currently used, like RAGs, and our results are very promising.”
You may not be building a biological-persistence-inspired filter, but the architectural principle is worth considering for your own systems: what constraints, rules, or sanitization logic can you apply to inputs before they reach your LLM? A security layer that filters or normalizes context can dramatically improve the consistency of your model’s outputs, especially in domain-specific applications where accuracy is non-negotiable.
Watch Alberto’s full interview: Biologically Inspired AI Guardrails?
5. Treat Localization and Language as Core Design
If your application will serve users in non-English-speaking markets, the language your LLM was primarily trained on matters more than you might think. This isn’t just a translation problem, it’s a model calibration problem.
Spanish is the second most widely spoken native language in the world, with nearly 500 million native speakers, and there are significant linguistic differences across regions. The word choices in Mexican Spanish differ from those in Colombian Spanish, Argentine Spanish, or the Spanish spoken in Spain. This is as much the way a Texan, a New Yorker, and someone from the Pacific Coast all use American English differently than someone from London.
Dr. Eduardo Perez, organizer of the IA Expo in Mexico City, told me about Celestial Dynamics, a Mexico City company building an LLM specifically calibrated for speakers of Mexican Spanish. Their reasoning: a generic model trained predominantly on English, or even generic Spanish, introduces subtle but real errors when used by people in specific regional markets, and those errors compound in professional contexts.
If you’re building domain-specific AI applications, such as healthcare, legal services, financial services, or any other field where precision matters, then the lesson extends further. Just as medical AI systems are fine-tuned on medical terminology and given tighter guardrails, regionally-deployed AI systems benefit from models that understand local language and culture. This is an active area of AI development, and teams in Latin America are ahead of many North American companies in thinking about it.
Watch Dr. Perez’s full interview: AI Engineering Beyond Silicon Valley
6. Make Iteration Part of the Product
The best AI applications being built today share one characteristic: their teams treat the system as something that is always being refined, not something that gets built and shipped.
Pablo Fajer’s journey from 42% to 92% accuracy took sustained investment in iteration: adding supervisor agents, building evaluation loops, incorporating feedback from human reviewers, and repeatedly testing against real-world scenarios. That is not a bug fix process. It’s an engineering discipline.
Dr. Eduardo Perez put the urgency of iteration bluntly: “If you have an idea and you don’t implement it in a few days or a month, you’re already behind.” The pace of change in LLM capabilities, tooling, and best practices means that waiting until you have the “right” architecture figured out before you build is a losing strategy. The teams that are ahead are the ones shipping early, measuring real-world performance, and iterating aggressively.
This has implications for how you staff and structure your AI development teams. You need engineers who are comfortable with ambiguity, who think probabilistically about system behavior, and who are as focused on evaluation and feedback infrastructure as they are on features.
Building LLM-driven applications is hard. Here’s how to do it right.
The technical challenges of building reliable LLM-integrated applications are real. Hallucination, accuracy drift, context management, safety layers, model selection, language calibration — these are not small problems. But they are solvable problems, and teams around the world, including in Latin America, are building the expertise to solve them.
If your organization is ready to move from AI experiments to AI products, the right team makes all the difference. Whether you need experienced AI engineers embedded in your existing team, a partner to build your first AI prototype, or the infrastructure to build a full AI engineering capability center, AgilityFeat can help.
AgilityFeat builds AI-driven applications and helps companies scale technical teams across Latin America. If you’re ready to move from AI experiment to AI product, contact us today to talk about how we can help through Staff Augmentation, a Build-Operate-Transfer model, or a Builder Pod to build your AI prototype.
Author’s note: Arin Sime is the Founder of AgilityFeat and host of the where he interviews engineering leaders about scaling teams and building great products. The Mexico City episode series referenced in this post is available on Spotify, Apple Podcasts, and YouTube. For more details on the guests, see the show notes for AI Engineering in Mexico – Part 1 and Part 2 at ScalingTechPod.com.
Further Reading:
- How to Hire LLM Engineers: Why Latin America Solves the AI Talent Crisis
- Building Production-Ready LLM Applications with Nearshore Teams: From Generative AI to Voice Agents
- Proof of Value vs. Proof of Concept in the Age of AI
- 2 Week Hybrid AI POC: Prototyping a Startup Concept with Smart Integrations and the Right Use of AI
- Best Practices for AI-Assisted Software Development
- When Is the Right Time to Scale Your Software Development Team?
