In an overheated AI market, speed often beats substance. The clearest red flags to avoid, and green flag practices to adopt, are emerging among teams building AI that’s grounded in reality, rigorously tested, and designed to last.
Grounding AI in the Real World
Red flag: AI that can’t check itself Models that aren’t connected to live data or process off of static training are more likely to hallucinate data, speculate, or give answers that aren’t up to date
Green flag: AI connected to live data One of the clearest shifts among leading AI teams is moving beyond models that rely solely on what they “remember” from their training. Instead, many are adopting Retrieval-Augmented Generation (RAG) to connect the model to the real world. Think of it as giving the AI an open-book exam with a live connection to the internet or your company’s private data.
This simple shift is a game-changer for substance. While a standard AI has a “knowledge cutoff,” and can’t tell you what happened in the world 10 minutes ago, RAG lets the model process information that is only milliseconds old. Real-world testing from C3 AI shows that their RAG-enabled systems reached 91% total accuracy, more than double the performance of the generic model baseline. To sum it up, RAG transforms AI into a high-speed librarian that cross-references itself and reassures users that the output is a verified response.
“Death to Slop” Development
Red flag: Demo-driven development Relying on vibe-checks or falling into the buzzword trap is a one-way ticket to slop city. Going off cherry-picked examples or measuring without high-precision allows AI to start rambling, hallucinating, or defaulting to buzzwords.
Green flag: Rigorous evaluation for every release The top teams treat models like high-stakes software engineering. Advanced frameworks used with human testers run tens of thousands of tests on every update that can catch even subtle errors. It’s not basic questions; they measure hallucination rates, consistency bias, toxicity, and data accuracy with increasing precision. If a model starts to go awry giving speculative claims or generic answers, the team will flag it and the model never sees the light of day. It’s a strict quality filter that ensures only the best versions are released for public use.
Mastering the Craft
Red flag: One model to rule them all There are a lot of generic AI models out there that are good for general information and surface level aid. However, when these models lack context or information, they often guess what they can’t find. The broader the scope of the model, the higher the risk of shallow or misleading outputs; something that really wastes effort in professional settings.
Green flag: AI with your name on it Top companies are narrowing their focus and developing integrated products for niche industries. It’s a straightforward way to ensure quality because a model trained for one specific job, like finance, sales, or medicine, is always going to have a higher density of useful information than a generic LLM trying to guess or search for information that could be wrong.
For example, research into niche models like BloombergGPT shows that specialized models can be up to 50x smaller than general-purpose giants while being much more accurate in their fields. In addition to that, because of their specialization, these models are also often faster by about 30% and more direct than generic models that like to ramble. These specialized tools cut out the noise and provide sharp insights that general models just can’t.
Digital Sovereignty and Transparency
Red flag: Artificial Intelligence has positively impacted the workplace since its first adoption. However, relying on open, cloud-based models limits transparency, control, and flexibility. To someone who values their sensitive data being kept safe, these things matter.
Green flag: Sovereign systems In Europe, companies like Mistral AI are fighting low transparency and promoting user empowerment. By offering “open weight” models, they allow businesses to see exactly how the model was built and host it on their own private servers. When a company owns the model, they can tune out the annoying politeness and robotic “AI-isms” that can plague cloud-based models. The result is a tool that actually speaks the language of the company.
In addition to just sounding better, the company also receives total data sovereignty, ensuring that sensitive data remains in safe hands. While setting it up does take some technical skill, the return on investment can be worth it: running your own model for high-volume work can be up to 80% cheaper than paying a subscription to a generic provider. It’s the difference between renting an apartment and owning a custom home.
The Bottom Line
In an increasingly crowded AI market, the teams pulling ahead are building with clear metrics, strong governance, and real-world grounding from day one.
As AI becomes infrastructure, discipline beats hype. Discover how the leaders getting it right are building what lasts — and join the conversation at VivaTech 2026, where the future of AI moves from promise to practice.
Want more on AI in action? Discover how it's transforming retail.
