Innovating Too Fast? LLM-Powered Applications Need Evaluation, Not Just Acceleration
Anyone can plug in a new AI model. Not everyone can prove if it works
It feels like every time I blink, there's another headline screaming about some mind-blowing new Large Language Model. In my line of work, trying to keep up with the constant releases from the big tech players feels less like actual progress and more like we're all just sprinting. We all saw what the folks at DeepSeek did, how they made the world stop and evaluate everything they knew about LLMs.
But this crazy speed makes you wonder: How fast is too fast?
In this thrilling race, with contenders like OpenAI, Google AI, Claude, DeepSeek, Alibaba etc constantly pushing the boundaries, is innovation outpacing our ability to use AI effectively and what's holding us back?
The Accuracy Bottleneck: The Primary Hurdle to Production Deployment
So, while there are a bunch of reasons why LLMs aren’t yet widely used in production applications, besides the financial aspect, the biggest one has to be making sure that they are actually right.
This accuracy problem has a few layers:
The Inherent Probabilistic Nature: LLMs are just guessing the next word, not verifying facts.
Too much or too little context: If you do not give them enough information, they assume false information. Give them too much, and important details get lost.
The "Hallucination" Phenomenon: Sometimes they make some stuff up that is not true.
Knowledge Cut-offs: Most models do not know anything after their training cutoff date.
Bad prompts = bad answers: Vague instructions and missing context will tank your outputs, no matter how fancy the model is.
No feedback loop: If you're not checking what the model is saying, or grounding it in real data, then you're just winging it.
The problem might be you (sorry): Sometimes it’s not the model; it’s the way you are using the LLM, the lazy retries, the lack of testing, or just not thinking through the solution.
While legal copyright issues, data privacy and security risks, and concerns about bias and ethical implications are important, accuracy is often the first hurdle we have to jump over to actually use these models in production.
From Vibe-Coding to Verified-Coding: How to Evaluate Your LLM-Powered App
I am not going to walk you through how to use each of these evaluation techniques, but it is important to know what options are out there. Because if you are not using some form of evaluation, you are basically just guessing whether your LLM system is working. And before we get into how to evaluate, let’s ask the most basic question: do we even have the data to evaluate with?
And when I say “data,” I do not mean training data, I mean evaluation data. Think of it like a test paper you’re handing to your LLM: if you do not have clear inputs and expected outputs (like actual answers), how can you know if your model is doing its job?
Now, what if you do not have this kind of data? Should you start panicking? Nope. Modern LLM tools are a bit more forgiving than we give them credit for. Even without a fancy benchmark or a gold-standard dataset, there are still ways to evaluate your system:
1) LLMs as Evaluator
Sometimes, the only way to evaluate your LLM related solution is using another LLM. Like how teachers made students grade each other’s test: make these powerful models evaluate their own outputs (or the outputs of other models).They can grade answers, rank completions, or even score responses based on criteria like helpfulness, correctness, tone.
It is good when you do not have ground-truth data but still want some form of structured evaluation. You can research the concept of LLM-as-a-Judge or LLM Evaluators to learn more about this technique.
Tools that help:
AWS Bedrock Evaluation (bonus: built-in human + AI feedback loops)
2) Benchmarks
If you are ever wondering how your model stacks up against others on known tasks, benchmarks are your best friend. These standardised datasets are designed to test specific aspects of model performance, so you can get a clear picture of where your system excels or falls flat.
Popular benchmarks:
BIRD SQL: Text-to-SQL tasks
ARC: Used to test real reasoning, not just memorisation
SWE: Software Engineering benchmark, testing various aspects of software development, problem-solving, and coding skills
Just search a benchmark for a solution that you are working on and you might just find it.
3) Automated Metrics
These classic tools give you quantifiable scores to quickly compare outputs. Are they perfect? No. But they will definitely save you some time when you need to get a fast read on your system’s performance. If you want to dive deeper into how various metrics work and compare, check out this resource: LLM evaluation: Metrics, frameworks, and best practices.
Some go-to metrics:
ROUGE-N / ROUGE-L: Best for summarisation and translation, measuring overlap and fluency.
BLEU: Focuses on translation quality by checking how well the model matches reference text.
BERTScore: Assesses semantic similarity, useful for evaluating nuanced or open-ended responses.
Perplexity: Checks how well the model predicts the next token, mainly for training diagnostics, not task accuracy.
4) Human Evaluations
Let’s be real; automated metrics are great, but they can only get you so far. Nothing, and I mean nothing, beats human feedback. Especially if that feedback comes from domain experts or actual users. Why? Because sometimes, no matter how good your model or metrics are, they will miss something important that only a human can catch.
When Accuracy Isn’t Guaranteed: Build for Risk
Even with the best models, there is always a chunk of time where things just would not work right. So how do we dial down the chaos without losing our minds (or users)? Here’s what worked for me:.
Keep humans in the loop. Sometimes you just need a real person to sanity-check things. Also, it is easier to blame “human error” than explain model hallucination in a meeting.
Always have a fallback. LLMs will fail. Build safe defaults, backup flows, or even traditional logic to catch the mess.
Go back to OG design patterns. Design for change. That means flexible, modular code. Want to test a new model? Make it easy. Think factory patterns, clean interfaces, and support for multiple LLM clients right out of the box.
Go slow to go fast. Iterative development means fewer disasters. Ship small and test often.
Making LLM powered systems production-ready is not about eliminating risk, it’s about managing it.
Conclusion
Let’s be honest, swapping in a new model and crossing your fingers is not a strategy (unless your strategy is chaos). This LLM gold rush has made us all feel like we have to move fast or die trying. But real progress isn’t about how quickly you adopt new models, it’s about whether your systems actually improve. Exploration is great, adoption is necessary. But value? That comes from evaluation.