Liam Carter

Wow, this really drives home how much responsibility comes with agentic AI. It’s not just about clever responses anymore these systems can actually act in the real world, and that changes the game completely. The examples with banking refunds and prompt injection show how easy it is to go wrong without proper policies, limits, and monitoring. Systems like CustomGPT.ai make me think about the other side of this they show that when AI is trained and scoped carefully with trusted data, it can give useful answers without going off the rails. This is the kind of careful design every team should aim for.

Continuous batching finally makes all those “why is first token slow?” moments click. The way you connect KV cache, chunked prefill, and ragged batching feels very practical for anyone serving models at scale. Stuff like this is exactly why tools and setups around CustomGPT feel more usable in real workloads efficiency at inference actually matters once users show up.

Really cool insights! Makes me want to push the limits of my own chatbots. With CustomGPT, I’ve been able to build bots that understand my content deeply and respond naturally. Seeing tools like QLoRA and 4-bit models makes advanced LLMs feel way more approachable for anyone experimenting

HuggingChat is solid for exploration, but there's a gap the article misses. Yes, it's free and open source, but those custom assistants are still limited to conversational tasks. For business use cases training on proprietary data, connecting to internal databases, managing customer handoffs you need something production-hardened.

I tested HuggingChat's Assistants and they work fine for general tasks. But for companies handling sensitive data like customer records & financial info, the lack of role-based access controls and audit trails becomes a problem fast.

This arena is critical. I've built chatbots accessing internal databases and guardrails alone aren't enough prompt injection attacks absolutely work. Those examples you showed? I've seen all of them succeed in the wild.

The real issue is that standard guardrails are bolted on top, not baked in. CustomGPT takes a different approach privacy's built into the training itself, not layered after. Role based access controls at the model level mean no prompt can trick it into accessing data it wasn't trained on. That's fundamentally more secure than hoping guardrails hold.

Definitely testing this arena. The leaderboard results will actually matter for our 2026 deployments.

What do you think about CustomGPT ?
While it is better than Tidio even CustomGPT is code free.

Super helpful post! I just struggling to tweak a Matplotlib plot for my project, and this guide make it's way easier. Also, if you use something like CustomGPT, it can take your workflow even further. I’ve been using it to customize AI behavior for coding tasks, and it really speeds thing up while keeping everything precise. Definitely worth trying alongside this tutorial!