01 / Define the behaviour gap
Define the behaviour gap
Evaluate a base model on representative tasks and identify repeatable weaknesses that training could reasonably address.
LLM Fine-Tuning
Explore fine-tuning when a base model needs more consistent task behaviour, terminology, or output structure. Establish whether tuning adds value beyond prompting and retrieval before investing in training.
AI Transformation / Specialist service
Adapt model behaviour to your domain.
Explore fine-tuning when a base model needs more consistent task behaviour, terminology, or output structure. Establish whether tuning adds value beyond prompting and retrieval before investing in training.
Discuss your project02 / Prepare the training material
Prepare the training material
Review examples for consistency, rights to use, sensitive information, and coverage. Separate training and evaluation data.
03 / Compare before deployment
Compare before deployment
Test the tuned model against the baseline for quality, robustness, latency, and serving requirements before deciding how to release it.
04 / Your project
Talk through your use case
Discuss llm fine-tuning in the context of your current systems, your team, and the outcome you need. Define a focused starting point before implementation.
Talk to DEQXClarity before commitment.
Shape the engagement around the work you need. Specific deliverables, access, responsibilities, and acceptance criteria are agreed during discovery.
What we start with
- Workflow examples and process owners
- Approved data and system access
- Quality, security, and review requirements
What the scope can cover
- Define the behaviour gap
- Prepare the training material
- Compare before deployment
What we agree together
- A defined scope and prioritized delivery plan
- Review points and acceptance criteria
- Documentation and ongoing ownership
A practical path from idea to operation.
A staged approach gives your team clear review points. Timing and responsibilities depend on the scope and the readiness of the systems involved.
01
Define the task
Agree the workflow, users, data sources, and the evidence needed to judge whether the approach is useful.
02
Test the approach
Evaluate representative examples, failure cases, and human review needs before expanding the scope.
03
Build and integrate
Connect the agreed capability to the application and its permitted tools, with access and exception handling defined.
04
Prepare to operate
Document evaluation, monitoring, ownership, and the improvement cycle needed after release.
Before we begin
Useful answers. Clear expectations.
A few questions to help frame your llm fine-tuning project.
When is retrieval a better choice than fine-tuning?
Retrieval is often more suitable for frequently changing facts or access-controlled knowledge. Fine-tuning is considered for behaviour and task consistency. Some applications use both.
How is the scope and delivery plan defined?
We begin with your current situation and the outcome you need. For this service, the discussion covers define the behaviour gap, prepare the training material, compare before deployment. Deliverables, dependencies, review points, and commercial terms are agreed before implementation.
What happens after the initial delivery?
Handover, maintenance, and ongoing improvement are discussed as part of the engagement. The arrangement depends on your internal team, the systems involved, and the operating responsibilities you want DEQX to support.
Let’s talk about your project
Bring us the challenge. Let’s shape the next step.
Tell us what you want to improve, what you have today, and the constraints that matter. We’ll help define a practical starting point.
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