MODEL ENGINEERING & HUMAN OVERSIGHT
LLM training and evaluation
LLM training adapts model behaviour to a defined task using representative examples. NexWerk focuses on domain-specific adaptation of existing models, with data preparation, fine-tuning and evaluation scoped to the workflow. We compare simpler approaches before recommending a training programme.
Discuss Your Project
Fine-tuning, retrieval or prompting?
The right method depends on the failure you need to fix. More training is not automatically the best way to improve an AI application.
- Prompting baseline
- Start with clear instructions, examples and structured outputs. Establish baseline quality before adding data pipelines or training costs.
- Retrieval-augmented generation
- Use retrieval when answers depend on changing documents or private knowledge. Source permissions, citations and retrieval quality remain essential.
- Fine-tuning
- Consider fine-tuning for repeated behavioural requirements, terminology or output conventions that a baseline cannot reliably achieve. It does not guarantee factual accuracy or replace access controls.
How we scope model adaptation
- Define success: tasks, intended users, unacceptable errors and operational constraints.
- Prepare data: review usage rights, remove unnecessary sensitive information, resolve inconsistent labels and hold back an evaluation set.
- Compare approaches: test prompting and retrieval against the proposed adaptation method.
- Measure tradeoffs: evaluate task quality, latency and cost using representative cases and difficult edge cases.
- Prepare release: version the configuration, document limitations and define monitoring and rollback criteria.
What evidence should you receive?
A useful handover records data provenance, the training or adaptation configuration, evaluation methodology and a comparison with the original baseline. It also identifies failure cases and operational limitations. Acceptance criteria should be agreed before the work begins, not selected after seeing the results.
Common questions
Do you train foundation models from scratch?
This service focuses on adapting existing models. Training a foundation model from scratch requires a separate feasibility assessment covering data rights, compute, staffing, cost and safety evaluation.
Will private data train a public model?
Provider training terms, retention settings, hosting and processing agreements must be reviewed before any transfer. The answer depends on the selected deployment and contract; it should never be assumed from a product label.
Can evaluations cover different languages and sectors?
Yes, where appropriate evaluation data and domain reviewers are available. Each target language and use case needs its own testing. Healthcare, military support and government workflows require context-specific review rather than a general performance claim.
How is security checked?
Combine task evaluation with LLM security testing for prompt injection, data exposure and tool permissions. A model that answers accurately can still be part of an insecure application.
Connect the model to the workflow
Explore industrial AI workflows and NexWerk services. Bring a representative task, available data, deployment constraints and a measurable objective to the first discussion.
Discuss Your Requirements