0%0%Out of the box, every LLM is a generalist. Your product needs a specialist: your tone, your formats, your accuracy bar, your cost ceiling. Dezvo customizes LLMs systematically — prompt engineering with version control, fine-tuning when the data justifies it, eval suites that prove improvement, and guardrails that hold the line in production.
Prompt engineering shapes model behaviour through instructions, examples, and output constraints — it's fast, cheap, and reversible, and it solves the large majority of quality problems. Fine-tuning retrains the model's weights on your examples — it's the right tool when you need consistent style at scale, a smaller/cheaper model to match a bigger one's quality on a narrow task, or behaviour prompts can't reach.
The honest sequence is: engineer the prompts, build an eval set, measure — and only fine-tune if the eval says prompting has plateaued. Teams that fine-tune first burn months and training budgets on problems a better prompt would have solved in a week. We always run the eval first.
System prompts, few-shot examples, and output schemas — versioned in git, A/B tested against the eval set, never tweaked blind in a playground.
Dataset curation, training, and validation on OpenAI fine-tunes or open-source models (Llama, Mistral) — when evals prove prompting has plateaued.
A frozen test set of real cases scored on accuracy, format, tone, and safety — run on every prompt change, model upgrade, and deploy.
Input/output validation, PII redaction, jailbreak resistance — plus routing, caching, and token diet to cut cost without cutting quality.