Many tech professionals see integrating large language models (LLMs) as a simple process -just connect an API and let it run. At Wallarm, our experience has proved otherwise. Through rigorous testing and iteration, our engineering team uncovered several critical insights about deploying LLMs securely and effectively.
This blog shares our journey of integrating cutting-edge AI into a security product. It’s also a testament to the Wallarm engineers who tackled each challenge head-on, often working with technology that wasn’t ready-made or safe by default.
I’m grateful for their dedication. If you’re an engineering leader or an AI practitioner navigating similar complexities, hopefully our experiences can provide some guidance.
1. The Myth of the Perfect Prompt
Early on, we wanted to believe in the myth of the “perfect prompt.” Write it well enough, and your LLM will answer anything accurately, right? Unfortunately, the reality is that even the best prompt, for the simplest task, will still get things wrong. Sometimes hilariously. Sometimes dangerously.
In security, a single miss means a threat slipping through. That’s why we never settled for “one and done.” Our engineers built pipelines where every LLM output is validated multiple times, often by additional models and adversarial modules. We drew inspiration from ensemble theory and backed up our work with the latest research from Microsoft, DeepMind, and others like Reflexion, and AutoGPT.
For example, when classifying an attack payload, one LLM makes the call, a second re-evaluates it, and a third adversarial module may attempt to “jailbreak” or bypass the result. This layered architecture isn’t just a nice-to-have; it’s a necessity.
What’s the lesson here? In production, safety is multiplicative, not additive. One prompt is never enough.
2. LLMs Are the Best Prompt Engineers
One of the most humbling moments for me as a founder came when our engineers showed me that LLMs, given structured feedback, could tune and optimize their own prompts better than any human (even our most senior prompt writers).
Here’s how it works: we feed the model its own failure cases with meta-instructions to revise the prompt. The output is reviewed, tested, and often deployed. The improvement curve was dramatic. Prompt revision became faster, more reliable, and even more creative. This is backed by research papers like Self-Refine and Promptbreeder, and our own experience confirms it.
Don’t confuse this process with surrendering control. It’s about leveraging the model’s strengths and iteration and recognizing when new tools are simply better at some tasks.
3. Prompts Need Their Own CI/CD
Shipping a new backend API? You wouldn’t push to production without tests, logging, and rollback strategies. Yet many teams ship prompt updates with zero validation.
At Wallarm, we treat prompts like code. Every change is regression-tested against thousands of historical artifacts, known threat patterns, and edge cases. We shadow-deploy prompts before cutting them into production, measuring both accuracy and semantic drift.
We’ve caught countless edge case regressions this way. For example, a prompt that appears to perform well on recent or typical inputs but quietly fails to detect critical, rare attack payloads that we encountered six months ago. These failures don’t show up unless you explicitly test for them, which is why automated, historical regression testing is essential.
The lesson here is that it’s essential to test and monitor prompts continuously. In security, trust must be earned, not assumed.
4. Token Economics Shift Overnight
It’s easy to obsess over token costs, API quotas, and model pricing. But in our experience, these numbers change faster than any roadmap can keep up with.
The real priority isn’t cost savings, it’s capability. The most impactful production wins came from prioritizing model quality, even if that meant temporarily higher expenses. And, as we’ve seen, the same LLM features can become 10x cheaper just months later as providers release more efficient models.
Think about it this way: quality and correctness come first, and economics will follow.
5. The Hard Part is Still Human
The key takeaway here is that the bottleneck is not the LLM, it’s the mindset around it.
I’ve seen brilliant engineers write off LLMs as untrustworthy after a few poor outputs. But these systems are like junior engineers: they need coaching, feedback, and guardrails. LLMs can learn endlessly, never tire, and improve quickly – but only if your team has the patience and process to support them.
At Wallarm, we built documentation, feedback loops, and shared internal tools to support LLM development. Our engineers didn’t expect perfection. They built processes to iterate, test, and learn, treating the LLM not as a magical oracle but as a teammate. Ultimately, the model is only as effective as the team deploying it.
A Personal Thank You
There are many days as a CEO when the work feels abstract. It’s all strategy, numbers, and investor decks. But seeing what our engineering team has accomplished, pioneering in a space where the tooling is immature, the playbooks aren’t written, and the threats are real? That’s humbling.
Wallarm’s engineers don’t just use LLMs, they stand between our customers and adversaries who are learning to use these same models for attacks, evasion, and jailbreaks. We can’t afford to wait for perfect tools. We build them. We stress-test them. We make them safer.
To the team: thank you for your audacity, your skepticism, your refusal to cut corners. You are the reason our customers can sleep at night.
To the broader community: the future of AI in security won’t be built alone. If you’re experimenting with LLMs in production, we’d love to exchange notes.
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