Arabic needs context, not translation
Dialect, measurement, and user experience in conversational automation.
Arabic in production is rarely MSA-only. Gulf customers write the way they speak; a bot trained on formal news corpora can sound grammatically correct and still fail the conversation.
We pair dialect-aware design with evaluation: grounded answers, clear handoffs to humans, and model choices that survive cost and compliance reviews — the essays below walk through that stack.
- GPT-4 vs Claude vs Gemini — an objective comparison.
This is not a popularity vote. It is a decision frame: what differentiates each family, where each leads, where each weakens, and how to choose without buying the myth of a single "best" model.
- How the Transformer works — a plain-language guide.
"Attention Is All You Need" changed the industry, but it does not belong in a product review meeting. This is the version for builders: one mechanism called attention, reweighting importance between tokens based on context — without a single equation.
- Why most Arabic AI bots fail.
It is not the model. It is that we train it on Arabic no one actually speaks, then act surprised when no one understands it back.
- What prompt injection actually is — before you flip on tools.
A blocklist stops neither an adversary nor a clever employee paste. Strings merge in one stream; attackers hide instructions inside email your assistant ingests quietly.
- Grafana for LLM stacks — what you must chart before you blame the GPU.
HTTP 200 is not cognition. Separate edge latency from inference backlog, KV pressure, retrieval lag, then token-dollar math on one executive wall.
- Running an LLM in Oman — year-one economics without the theater.
Hardware, colocation, industrial power, three operator roles, GPU failure—then compare with an API line that still respects PDPL and cross-border reality.
- Why the Gulf still does not ship one federated Arabic ChatGPT — honestly.
It is sovereignty seams, sovereign wealth magnetism toward US hyperscalers, GPU scarcity politics, procurement theatre—before the brand halo consolidates.
- Qwen2.5-72B vs GPT-4o — which wins for Arabic.
Internal benchmark snapshot on Arabic office reality: GPT-4o strength on fusḥā and numerics, open-weight upside on sovereignty and throughput — with one chart to align execs.
- What Is KV Cache in LLM Inference and How Does It Eat VRAM?
The GPU is not the whole truth — part of inference speed is reusing intermediate keys and values instead of recomputing layers for every token.
- Enterprise Prompt Injection: Defence Layers Beyond Word Blocklists.
A word list won’t stop instructions hidden in innocent sentences — real defence separates privileges, judges retrieval, and logs manipulation like classic intrusions.
- MLOps vs DevOps for LLM Production: Where the Difference Starts.
Shipping a container once is not operating AI — real ops means model versions, data roll-forward, drift monitoring, and rollbacks unlike a classic API redeploy.
- Inference vs training for LLMs — who pays for what.
Training might run once (or for many hours) and you pay a cluster bill. Inference runs forever and turns a model into a per-token Opex line. This article separates the two checkbooks so pilot budgets are not mixed with product bills [1].