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Building your own LLM without serious testing is like launching a rocket and hoping for the best.
According to the latest Galileo report (Q1 2026), 85% of teams have faced at least one AI incident in the past 6 months.
Do you know what the most dangerous trap is?
Overconfidence!
Companies that think their scenarios are “safe” end up with 11% more issues than those who honestly admit they didn’t have enough time for testing.
Why did the market leaders (elite teams) become leaders?
Because they take testing seriously.
They cover 90–100% of AI behavior with tests.
They spend over 40% of development time on evaluation (evals).
The result: their solutions are 2.2× more reliable than the market average.
It’s time for businesses to accept a simple truth: if your AI is “silent” about errors, it usually says more about poor diagnostics than actual quality.
Real reliability isn’t about having no bugs. It’s about a system being able to detect them before your users do.
Testing isn’t a boring report at the end of the quarter. It’s the only way to turn an expensive toy into a real business tool.
Honestly – would you trust your product to a model that isn’t tested systematically?
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This project focused on building and testing a practical AI security assessment lab for evaluating LLM defenses against prompt injection and jailbreak attacks.
I integrated Spikee by Reversec with a locally hosted cybersecurity model running through LM Studio, then added NVIDIA NeMo Guardrails to compare model behavior under three conditions: no guardrails, input filtering, and combined input/output protection.
The work included configuring the local model environment, building a custom FastAPI gateway, integrating NeMo Guardrails, troubleshooting model latency and timeout issues, creating a reusable Spikee target, and analyzing attack results using Spikee’s built-in reporting tools.
The project also explored different adversarial testing approaches, including prompt injection datasets, obfuscation, encoded attacks, Best-of-N testing, synthetic canary leakage tests, and structured benchmark comparisons.
The objective was to measure how much the guardrails reduced successful attacks while keeping the model, dataset, and testing conditions consistent.
Tools used: Spikee, NVIDIA NeMo Guardrails, LM Studio, Python, FastAPI, PowerShell, Parrot OS, local LLMs, JSONL datasets, and custom security testing scripts.
This project demonstrates a hands-on approach to LLM red teaming, AI safety testing, prompt-injection assessment, and guardrail validation for organizations deploying generative AI systems.
pretty cool tbh. testing the same attacks with different guardrail setups makes the results way more interesting.
i once worked with a founder who was about to start testing his product with 4–5 real industry users.
they were super early and b2b btw.
then his internal team started talking about analytics
→ what should we track?
→ what KPIs matter?
→ what should go into posthog?
i had to weigh in, “it’s too early for that” i said.
told them that at this stage (honestly at any stage) we had to do only 2 things…
1. watch what they do
→ where they stopped
→ what confused them
→ what they completely ignored
2. talk to them
→ we’ll understand the problem we’re solving better
→ we build rapport and trust. they tell us what’s actually bothering them
that’s it.
you don’t need more data, especially when you’re this early.
AI isn’t replacing designers.
but ignoring AI is a mistake.
in our latest UnContrad interview, @pizzaboy and @LukeADesign
break down what actually protects a creative business:
- strong standards
- better systems
- client-ready brands
the courage to reject “good enough”
this conversation is for every independent creative building something real ↓