Consulting · AI Systems
Training Your Own Models
Teach a model your domain, on your terms.
Retrieval gives a model access to the firm’s knowledge. Training gives it the firm’s judgment: its terminology, its document formats, its way of assessing a credit or writing a memo. Done well, a smaller model tuned on the firm’s data can outperform a much larger general one on the firm’s tasks, at lower cost and entirely under its control. Done badly, it is expensive and proves nothing.
When training beats retrieval
Retrieval is the right answer when the need is access to facts. Training is the right answer when the need is behavior: consistent style, domain vocabulary, structured outputs, or a task the general model does poorly no matter how much context it is given. Many production systems use both: a tuned model reading retrieved documents.
Methods
Full fine-tuning updates every parameter and is rarely necessary. Parameter-efficient methods adapt a small number of added weights and can be run on modest hardware. Distillation trains a small model to imitate a large one on the firm’s task, capturing most of the capability at a fraction of the inference cost. Preference tuning aligns outputs to what the firm’s experts actually approve.
Data and evaluation
Training data is the firm’s own labeled examples: past memos, approved outputs, expert corrections. The evaluation set is built first and held out, so that improvement is measured against the firm’s definition of good rather than a benchmark. A model that cannot beat the baseline on the evaluation set does not ship.
Where it runs
Training on sensitive data happens in the firm’s environment or a private cloud tenancy under its keys. The resulting weights are the firm’s property, versioned and stored like any other critical asset, and the model is served from infrastructure the firm controls.
Practice
How Wesvel approaches it
- 01A held-out evaluation set defined by the firm’s experts is built before any training run.
- 02Parameter-efficient tuning and distillation are the defaults; full fine-tuning is used only where they fail.
- 03Training data never leaves the firm’s environment or its private tenancy, and the resulting weights are the firm’s property.
- 04Models are versioned, evaluated on every change, and retired when a newer base model beats them on the firm’s tests.
More in AI Systems
- Legacy LLMs vs Open-Weight ModelsWhat hosted frontier models and open-weight models each do well, what they cost in money and control, and how to use both without being captured by either.
- Own Your DataKeeping proprietary data inside the firm’s perimeter, understanding what vendors may learn from it, and building retrieval so models use your knowledge without absorbing it.
- Building Internal SystemsThe architecture around the model: orchestration, tools, retrieval, evaluation, monitoring, security and the infrastructure to run it.
- Governance and ComplianceModel risk management, human oversight, record-keeping, vendor diligence and disclosure for AI in regulated financial businesses.
- AI in Financial OperationsThe use cases that hold up in investment, lending and advisory operations, and the ones that do not, drawn from how Wesvel uses these systems itself.