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Building AI features that hold up in production: deciding when an agent is justified, grounding answers in company documents with permission-aware RAG, choosing between prompting, retrieval and fine-tuning, evaluating hallucinations, comparing and running Arabic language models, and integrating LLMs with legacy systems.
Showing · AI engineering
No leaderboard can pick your Arabic language model. Compare exact checkpoints from Falcon, ALLaM, Jais and Fanar on your own tasks and dialects, under comparable conditions, and weigh license and hosting separately.
Running an open-weight Arabic-capable model yourself or calling a hosted multilingual model is an operational decision, not a ranking. Decide it from where data may be processed, the control you need, what your team can run and your own evaluation.
Use several AI agents when a task has well-defined subtasks and a measured single-agent baseline falls short. Coordination costs tokens, time and engineering, so budget every run and let evidence decide.
To evaluate an LLM application for hallucinations, test it on real questions with agreed reference answers, check every claim against its sources, score retrieval and answers separately and read the results by slice, not as one average.
Testing cannot remove hallucinations entirely, so production safety depends on what surrounds each release: a gate agreed in advance, staged rollout, monitoring of live answers, incident containment and a rehearsed rollback.
Choose internal AI copilot use cases by four properties of the workflow: maintained sources with owners, permissions you can enforce, an error cost you can absorb or catch, and a natural human review before the output takes effect.
Judge an internal AI copilot pilot by comparing accepted outputs and total cost against a baseline measured before launch. Usage counts do not prove value, and no return should be promised before the pilot has measured one.
Adding an LLM to a legacy application is mostly a boundary problem: where the model runs, whose identity it uses, which tools it may call and how changes reach your records. This reference architecture makes each boundary explicit.
