SCNET · Enterprise IT · Ankara, Türkiye
The difficulty in generative AI projects is not running a model but making its output trustworthy. Sanal Çekirdek builds the work around guardrails and evaluation sets.
Where answers must rest on knowledge, retrieval sits at the center of the architecture — we cover that on the Enterprise RAG page. This page is about the model itself, its boundaries and its cost.
Not every scenario needs the largest model. For summarization, classification and drafting, smaller models are often sufficient and cut cost several times over. Selection happens between measured quality and unit cost.
What the model will not do is defined as precisely as what it will. Input and output checks, an allowed topic boundary and a personal data filter are embedded; a request outside the boundary is refused rather than guessed at.
Whether a prompt change improved or degraded things is knowable only against a fixed evaluation set. Sets are built per use case, and every version is measured on the same one.
Cost rises linearly with usage, so caching, prompt length and model tier are managed together. Adoption, meanwhile, depends on users trusting the output rather than on interface polish.
No, unless explicitly agreed otherwise. For enterprise use, retention and training policy is written into the contract, and it is one of the constraints on model selection.
By grounding the model in sources and defining boundary behavior. Returning nothing when no source is found beats returning something wrong, and that behavior is explicitly tested.
RAG is the architecture that grounds answers in your documents. This page covers model selection, guardrails, evaluation and cost; in most projects the two are built together.
Let's pick one use case, write the acceptance criteria and measure it against an evaluation set — a system rather than a demo.
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