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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
Add an LLM to a legacy system in controlled stages: inventory touchpoints and data owners, pilot a read-only feature, run it in shadow mode, widen a routed rollout with rollback ready, and add write-back last.
Choose between prompt engineering, RAG and fine-tuning by diagnosing why answers fail: unclear instructions, missing knowledge or a consistent behavior problem. Then apply the smallest change that fixes the measured failure.
Enterprise RAG searches your own documents when a question arrives and gives the most relevant passages to a language model as context. Built well, it checks permissions before the model sees anything, cites what it used and declines when nothing it may use supports an answer.
Most business processes do not need a fully autonomous AI agent. This playbook helps CTOs decide when model-directed autonomy is justified, how to bound it with tool permissions and human approval, and which outcomes to measure before scaling.
