Frequently Asked Questions
What is generative AI?
Generative AI describes models that produce new content, whether text, images, audio, code or structured data, rather than classifying or predicting from existing inputs. The commercial distinction that matters is that the output is judged on quality and appropriateness rather than on being right or wrong, which changes how you test it.
How is generative AI different from an AI agent?
Generative AI produces output. An AI agent decides what to do and takes action, usually calling tools and systems to do it, and frequently uses generation as one step. If you need something written, drafted or created, this is the right page. If you need multi-step work completed across your systems, our AI agent development service covers that.
Should we use prompting, RAG or fine-tuning?
Prompting first, because it is cheapest to build and change, and it handles more than most teams expect once structured output and good examples are in place. Retrieval when the model needs facts it was never trained on, which covers most enterprise cases. Fine-tuning when you need a format or voice that prompting keeps drifting from, or when volume is high enough that a smaller tuned model is cheaper than a larger general one. These combine, and most production systems use at least two.
Who owns the output a generative model produces?
Commercially you generally own what you generate, but the detail depends on the provider's terms and your jurisdiction, and copyright protection for purely machine-generated work is limited in several countries including the United States. Where output is going into something you sell or license, we identify the specific constraints during scoping and document them, rather than leaving it as an assumption.
What about the data these models were trained on?
It varies by model and it matters more for images than for text. Some providers indemnify commercial customers against training-data claims and some do not, and open-weight models differ again in what their licences permit. Model choice is therefore partly a legal decision, not only a technical one, and we surface those differences before a model gets embedded in your product.
Will our data be used to train someone else's model?
Not on enterprise API tiers from the major providers, which contractually exclude customer data from training, though the default consumer terms are often different. If your data cannot leave your infrastructure at all, open-weight models running in your own environment are the route, with a real trade-off in capability that we will quantify rather than gloss over.
How do you stop generated content being wrong?
Grounding in retrieval is the main defence, so the model works from your documents instead of its memory. On top of that we add citation of sources, automated checks that claims trace back to retrieved content, format validation, and human review sampled toward the riskiest outputs. The residual error rate gets measured rather than assumed, because assuming it is zero is how wrong content reaches customers.
Can it match our brand voice?
Yes, and more reliably than a growing writing team. It takes reference examples of what good looks like, a scoring method to check output against them, and usually structured prompting rather than fine-tuning. Voice drift across large batches is the common failure and it is caught by measurement, not by rereading the prompt.
How much does generative AI development cost?
The build depends on how much grounding and review infrastructure the use case needs, since a system producing internal drafts is far simpler than one producing customer-facing content. Running cost is per output and scales directly with volume, which makes it the number to model first at high volume. Fine-tuning adds a training cost plus, in some cases, a higher per-token rate for the tuned model.
How do you control cost at high volume?
Route work to the smallest model that produces acceptable output, which for classification and extraction steps is often much smaller than teams assume. Cache aggressively where inputs repeat. Trim context, since prompts tend to accumulate examples nobody has revisited. Batch where latency allows. These decisions are made against measured usage rather than estimates.
Which models do you work with?
We stay provider-agnostic and choose per use case, mixing frontier models where output quality justifies the cost with smaller or open-weight models where it does not. Open weights become the clear answer when data residency rules out sending content to a third party, or when volume makes per-token pricing uneconomic.
Should we self-host an open model?
Self-hosting makes sense when data cannot leave your infrastructure, when volume is high enough that GPU cost beats per-token pricing, or when you need a model that will not change underneath you. Against that, you take on serving, scaling and upgrades, and the best open weights still trail the frontier on hard reasoning. We work the numbers for your volume rather than answering from principle.
What happens when the model provider updates their model?
Output changes, sometimes noticeably, and deprecation timelines are set by the provider. This is why the evaluation set matters: it lets you run the new version against known cases and see what moved before your customers do. We monitor for deprecations and handle migrations as routine maintenance rather than as an emergency.
How much human review does generated content need?
It scales with consequence, not with volume. Internal drafts and first-pass summaries can run unreviewed. Anything customer-facing, contractual or regulated should have a person in the path, at least until measured accuracy justifies loosening it. The practical answer is usually sampled review weighted toward outputs the system itself scored as low confidence.
Can generative AI work with our existing systems?
Yes, and that integration is where most of the engineering time goes. Generated content has to reach your CMS, product catalogue, CRM or support desk, and it has to carry review state with it so half-approved output does not get published. We build that plumbing as part of the work rather than leaving it as an exercise.
How long does a generative AI project take?
A focused feature running against your real content typically reaches a working prototype in a few weeks. Hardening it, meaning grounding, evaluation, review workflow and cost tuning, usually takes as long again. Systems spanning several content types and destinations run to a few months. We aim to get something real in front of you early because generative quality is difficult to judge in the abstract.
What do you need from us to start?
Examples of the output you want, ideally a dozen pieces a person produced well, because those become both the specification and the yardstick. Access to the source content the system should draw on. Someone who can settle questions of tone and correctness. The examples matter most, and their absence is the single most common reason a generative project stalls at the review stage.
Why choose Zyneto for generative AI development?
We try the cheapest approach first and say so when a fine-tuned model is not warranted. We measure output quality rather than judging it by eye, which is the only thing that scales past a few hundred generations. We give straight answers on model licensing and data rights before they become a legal problem. And we have shipped generative features inside a real product, which is a different discipline from building a demo.
