Generative AI with an editor in the loop

Start with prompts, examples and retrieval; they are cheaper to change and often enough. Fine-tuning helps when you need a consistent format or tone at very high volume, or want a smaller, cheaper model to match a larger one on a narrow task. It needs hundreds to thousands of high-quality examples and retraining when your style changes. We test both on your evaluation set before recommending it.

You own the outputs you commission from us, and major model providers’ terms generally assign output rights to the customer. Copyright protection for largely machine-generated material differs between countries and is still developing, so for important assets we recommend meaningful human editing. For images we check each model’s licence for commercial use. This is a practical view, not legal advice; your counsel should confirm it for high-value work.

Thin, repetitive or inaccurate text can; accurate, useful text generally does not, and search engines’ published guidance focuses on helpfulness rather than on how content was drafted. We generate from real attributes, vary structure across product types and have people review a sample of every batch. We avoid producing hundreds of near-identical pages, the pattern that tends to cause problems.

With your style guide turned into instructions, approved examples for each content type, and checks on vocabulary you do or do not use. Editors’ changes are logged, and recurring edits feed back into the prompts. For Indian-language content, fluent reviewers approve the reference examples, because a direct translation of an English tone rarely reads naturally in Hindi or Tamil.