---
title: The path to confidently scaling LLM applications
description: Ultimately, understanding the quality of an LLM system is not merely an engineering task, it also means forming a feel for what kind of product it should be, who should be using it and how. In the following, we explore the steps you can take in order to go from an AI demo to an AI solution in production.
image: https://brillian.fi/hubfs/Expert%20blog_samuel.jpg
---
[Skip to content](https://brillian.fi/blog/the-path-to-confidently-scaling-llm-applications#main-content)
- [Services](https://brillian.fi/services)
- [Our Story](https://brillian.fi/our-story)
- [Join Us](https://careers.brillian.fi/)
- [News](https://brillian.fi/blog/all)
Get In Touch
Get In Touch
# The path to confidently scaling LLM applications
[Samuel Rönnqvist](https://brillian.fi/blog/author/samuel-rönnqvist) [](https://www.linkedin.com/in/sronnqvist/)
•
Oct 9, 2026
Evaluation AI governance
This is the second post in the series Building trust when scaling AI. The [previous article](https://brillian.fi/blog/the-cost-of-neglecting-ai-evaluation) talked about the cost of not knowing: the way an LLM feature can lose users without ever producing an error, and the way a team can spend months improving something without being able to say whether it got better. A crucial point is that the useful question is not how often the system is right, as much as in what ways it is wrong. This post will take you through the journey towards that, understanding your product, and being able to turn that into a tool that helps you and your team scale up the use of LLMs in production.
## WELL-FORMED LLM OUTPUTS LOOK PLAUSIBLE, BUT CAN STILL BE WRONG
LLM applications and agents are in large part ordinary software. The orchestration around all LLM calls, context assembly, response parsing, tool calling and the control logic on top is all regular, deterministic code. These parts can fail in the ways software normally fails, and they benefit from the usual testing practices, such as unit and integration testing. Traditional software testing serves a role in checking the integrity of the plumbing and report basic metrics, but, while that guarantees a fundamental level of quality, it paints an incomplete picture.
While the LLM’s non-determinism is the source of its flexibility and power, it is also what makes understanding quality more difficult. Defining correct behavior is not straightforward when the space of possibilities is so immense. You cannot enumerate all situations the model will encounter, nor write the assertions that would cover them. The output might also vary from time to time for any given input, and deviate from whatever reference answer you have gathered. So how do you tell whether it is any good?
Ultimately, understanding the quality of an LLM system is not merely an engineering task, it also means forming a feel for what kind of product it should be, who should be using it and how.
In the following, we explore the steps you can take in order to go from an AI demo to an AI solution in production. You and your team might already have embarked on this journey of moving out of the safe development environment, towards facing real-world users, or you might be stuck somewhere on the way. If you can locate yourself on the path that we present below, you will learn what will give you the confidence to take the next step.
## The start: Capability
Typically the life of an LLM application starts here: as a prototype, or internal demo. Built on a controlled data set, i.e., inputs you chose yourself, from documents or questions you had at hand or could think of, it demonstrates AI capability for a specific use case. A good number of initiatives also stop here, because the path forward might be unclear.
Demonstrating feasibility is an important first step, as is creating a first version to iterate on. But testing it only on known inputs, which likely already informed the specification of the implementation, makes for a narrow and biased view. You need to put the system in front of real users eventually, and that will give you the chance to learn how it behaves in unforeseen situations.
At the same time, that may be exactly what you fear, putting a system you don’t fully understand nor control in front of real users. You may be dealing with a high-stakes use case, where incorrect answers can be directly damaging and costly, or at the very least you risk losing users who simply become frustrated in their early trials and give up. So the first step out of the controlled development environment focuses on establishing a basic level of safety, which lets you start running real experiments.
## Protection (guardrails)
Guardrails are cheap, deterministic checks that are always on in production. They provide you a basic level of protection for the types of failures that you are able to foresee, and that may be damaging in one way or another. Unlike unit tests, they don’t merely test behavior on predetermined inputs, but they define unacceptable behavior for any input, at any processing step. They define a low, non-negotiable quality bar.
When a guardrail fires, it may block the output, force a retry by the LLM, or escalate to a human. They can validate structured output from LLMs, guard against certain types of content, measure hallucination, etc. They can also provide informative feedback, for instance, helping the LLM to correct its own mistakes before showing it to a user.
With this level of safety in place, you effectively lower the risks associated with exposing real users to a new system, and you are able to start on-boarding in order to start learning from real data. The first set of guardrails come from your requirements, and as you start running experiments, watch the system be used and increase your understanding, you may extend and refine those guardrails. But, in order to watch the system, you need observability.
## Observability
Observability is about establishing infrastructure that gives you a clear overview of everything your LLM system is doing in production. It is about compiling complete traces of every execution step, not dispersed logs, and in this context it is about the content more than latency and cost metrics. It is not mainly focusing on errors in the traditional sense, because those are only part of the problem, but on supporting qualitative understanding by tracking the system’s behavior end-to-end during interactions.
This observability layer importantly records and gathers inputs and outputs, but also intermediate steps such as model reasoning, tool call requests, their execution and orchestration decisions. It lets you peek inside the machinery as it is handling real requests, and observe patterns you never were able to anticipate. This can give you real product insight by helping you spot obvious patterns, and is a prerequisite for evaluation.
## Judgement and systematic evaluation
As you get into a habit of observing your system under real use, you start to form judgements about whether the behavior is good or bad. You try to put yourself in your users’ shoes and understand what constitutes a good product. You develop a taste for how they will want to interact with the system. You may gather their feedback and also interact deeply with expert users in certain domains, in order to act as their proxy. At this point, AI development is no longer purely an engineering discipline, but a cross-functional effort that includes product questions.
This is the start of AI evaluation, and it is a step that involves systematically gathering and judging examples from real usage, in order to build an understanding of the ways in which the system fails. Mapping out these failure modes provides a qualitative understanding before measuring and calculating scores, because a score that is poorly founded and understood holds little information and is likely to misguide.
The understanding of failures that arises early from systematic evaluation can immediately guide development, and quantifying the failures only strengthens the evidence. However, in order to truly set off an iterative improvement loop, evaluation needs to be easy to repeat.
## Automated evaluation
Once you have manually evaluated a set of examples, and gathered an understanding for how your system works under real conditions, you will want to act on that and start improving it by debugging the failure modes that were surfaced. For that, you need to turn your judgements into a repeatable test, which can be easily rerun in order to track quality across iterations.
This is the step where the test turns into a score, with a well-understood meaning. The score provides direction in development by letting you track whether a change to any given part of the LLM system, be it in the prompts, the model, tools or orchestration, was for the better or worse. The score provides situational awareness and confidence to iterate, and the specific failures provide entry points for debugging. This is how evaluation enables the improvement loop.
Evaluation not only provides guidance on how to develop, but also how to deploy and scale usage. It is the key to answering questions such as “is this concept valid?”, by providing the proof for a Proof of Concept, and “is this ready to deploy to this audience?” when thinking about scaling. Evaluation is the tool that gives you overall confidence in the quality of your system.
## Where are you on this path?
Each step described above builds on the previous and forms a path. Starting in a controlled environment, it lets you gradually scale outwards to the real world. Hopefully, you will now be able to tell where you are in this process of maturing your LLM application, and what next step will be able to help you forward.
At Brillian, this is part of what we do, getting to know our customers’ situation and needs, in order to help them forward. We covered evaluation only briefly here. It is relatively easy to describe and considerably harder to do. The next post in the series drills into how it is done in practice.
*Samuel Rönnqvist is Lead AI Architect at Brillian. He holds a PhD in NLP and has spent the past decade building AI systems into production and researched AI explainability, with a particular focus on reliability and building trust in AI .* To discuss more, get in touch on [LinkedIn](https://www.linkedin.com/in/sronnqvist/).
###### Get in Touch
Interested in working together? Want to hear more about what we offer, and how it can best benefit you? Leave us your details, and we’ll get back to you!
###### Book a Meeting
Curious about how our services can help you reach and exceed your goals? Book a meeting with us, give us a call, or send us an email to learn more.
[Book a Meeting](https://brillian.fi/meetings/when/meet-with-brillian?uuid=35fcbf84-574d-4886-9ad8-1e6dfac5ff0f)
[+358 40 831 2762](tel:+358%2040%20831%202762) [brillian@brillian.fi](mailto:brillian@brillian.fi)
###### Follow us
Stay connected with us! Follow our social channels to stay updated on the latest news and how we can serve you better.
[](https://www.linkedin.com/company/brillian/)
```json
{
"@context" : "https://schema.org",
"@type" : "BlogPosting",
"author" : {
"@type" : "Person",
"name" : "Samuel Rönnqvist",
"url" : "https://brillian.fi/blog/author/samuel-rönnqvist"
},
"dateModified" : "2026-10-09T12:11:35.119Z",
"datePublished" : "2026-10-09T12:11:35.000Z",
"headline" : "The path to confidently scaling LLM applications",
"image" : [ "https://brillian.fi/hubfs/Expert%20blog_samuel.jpg" ],
"mainEntityOfPage" : {
"@id" : "https://brillian.fi/blog/the-path-to-confidently-scaling-llm-applications",
"@type" : "WebPage"
},
"publisher" : {
"@type" : "Organization",
"logo" : {
"@type" : "ImageObject",
"url" : "https://brillian.fi/hubfs/Logo_Darkblue640w.png"
},
"name" : "Brillian Oy"
}
}
```