TensorBlue · Model adaptation
LLM Fine-Tuning & RAG Integration Services
Adapt language models to your workflow with data preparation, reproducible training, task-specific evaluation, and deployment planning. Start with a baseline and measure whether the candidate earns a place in production.
Discuss your fine-tuning projectWhen is fine-tuning worth testing?
Start with an observed failure on a specific task: inconsistent output structure, domain terminology, classification, or a response style that prompting does not reliably produce. Fine-tuning adapts model behavior using examples. It does not automatically supply up-to-date knowledge or make every generated statement correct.
Compare a prompting baseline and, where relevant, retrieval-augmented generation before training. Retrieval can provide changing documents and source context; a fine-tuned model can still use retrieval. Choose the intervention that improves your measured task rather than choosing a training method first.
A useful discovery brief contains example inputs, acceptable outputs, common failures, deployment constraints, and the business consequence of mistakes. Include cases where the model should decline, ask for clarification, or hand work to a person.
Data preparation and ownership
Review whether you have rights to use the training examples. Define which personal, confidential, or proprietary fields can enter the pipeline, where processing takes place, who has access, and what must be deleted. Synthetic examples also need validation; generated data can repeat errors and should not be treated as verified labels.
Create a consistent labeling guide with positive examples and edge cases. Remove duplicate records, resolve conflicting labels, and inspect examples that contradict the desired behavior. Preserve a record of dataset versions, changes, and the source of each label.
Keep training, validation, and final evaluation data separate. Split related records together when needed to prevent the same customer, document, or near-duplicate example appearing in both training and testing. There is no universal example count that proves readiness: coverage and label quality matter alongside volume.
Ownership and distribution rights depend on the base-model license, provider terms, training data rights, and engagement agreement. An adapter is not automatically a license to redistribute its base model. Record these constraints before selecting the deployment architecture.
Training approach and model selection
Select candidate models against the task, license, language coverage, context requirements, hosting environment, and budget. Confirm that a hosted provider supports the exact model and training method before including it in a proposal. Model catalogs and availability change.
Parameter-efficient methods such as LoRA train a smaller set of adapter parameters while keeping the original weights frozen. This can reduce the training resources compared with updating all parameters, but task quality still needs evaluation. Full fine-tuning, adapters, or a simpler baseline should be chosen from evidence rather than a promised accuracy percentage.
Track dataset version, base-model version, training configuration, checkpoint, evaluation outputs, and resource usage for each experiment. Change one major variable at a time when diagnosing a failure. Stop a training run when the validation evidence does not support further investment.
How we evaluate a fine-tuned model
Agree on acceptance criteria before training. A classification task may need per-class precision and recall; extraction may need field-level correctness; generated answers may need a rubric for factual support, completeness, and refusal behavior. A single accuracy score can hide expensive failures.
Run the same held-out examples through the baseline and candidate. Review difficult cases and important subgroups separately. Report the test-set size, evaluation method, model versions, and failure categories with the results. Do not present a result from one task as an expected outcome for every customer.
Measure the full system as well as model quality: latency under expected traffic, resource consumption, error handling, source retrieval where used, and the cost per successfully completed task. Use human review for ambiguous or high-consequence outputs and define when the system must escalate.
For example, a support-labeling pilot could compare correct routing, false urgent flags, and manual correction time. This is an illustrative evaluation design, not a TensorBlue client result or a promised improvement. Decide the rollout threshold with the operating team before looking at candidate results.
Delivery stages and handover
Discovery: review the workflow and data access, reproduce baseline failures, agree on evaluation cases, and decide whether training is justified. Data preparation: establish labels, splits, permissions, and a versioned dataset. Experiments: train candidates and compare them with the baseline.
Deployment: integrate the selected model or adapter with the application, document inference settings, and test monitoring, access controls, fallback behavior, and rollback. Pilot: collect reviewed failures and operational measurements before expanding use.
A handover should identify the accepted model version, evaluation report, dataset manifest, training configuration, deployment instructions, known limitations, and responsibility for maintenance. New data or a provider change can require reevaluation. Support scope and response arrangements should be stated in the proposal.
Cost and timeline: what determines the estimate?
The existing service package uses a starting price of $15K for a focused engagement. A scoped proposal needs to distinguish data preparation, labeling, training experiments, evaluation, integration, hosting, and ongoing operation. This page does not quote a fixed final cost for an unspecified workload.
Model size, sequence length, experiment count, hardware availability, dataset review, and security requirements affect the estimate. A hosted model can add training and inference charges; a self-hosted model adds infrastructure and operational work. Compare the total cost of a successful workflow, including human review.
Share a representative task, permitted sample data, current baseline, expected traffic, and the intended deployment environment. The delivery schedule should include dependencies such as access approval and label review. Agree on milestones, acceptance criteria, commercial terms, and change control in writing.
Technical references and further reading
- Hugging Face: how LoRA adapters work
- Google: Rules of Machine Learning
- TensorBlue LLM architecture guide
- RAG integration services
Technical references reviewed 7 October 2026. Delivery guidance describes a proposed process; it is not a benchmark report or client testimonial.
Frequently Asked Questions
When should I fine-tune instead of using prompting or RAG?
Test fine-tuning when consistent task behavior or output structure remains a problem after establishing a prompting baseline. Retrieval can supply changing source material and can be combined with a fine-tuned model. Compare approaches on the same held-out examples.
Which models can be fine-tuned?
Selection depends on the task, license, deployment environment, and provider support for the exact model and method. Confirm current availability during discovery rather than assuming every hosted model supports training.
How much labeled data do I need?
There is no universal readiness threshold. Review label consistency, coverage of important failures, duplicates, and the independence of the final evaluation set. Use a pilot to assess whether additional examples improve the task.
What does fine-tuning cost?
The existing focused service package starts at $15K. The final estimate depends on data preparation, experiment count, evaluation, deployment, hosting, and support. Agree on scope and commercial terms in the proposal.