Model Customization & Fine-Tuning · Build
LLM Fine-Tuning & Model Customization
Fine-tuning is justified only after prompt and RAG baselines leave a repeatable gap, and every training run remains versioned, held out, and comparable.
A stable behavior gap has to earn the training experiment. Prompt and RAG baselines get the same held-out tasks first. If the gap remains material, each versioned run is weighed for lift, critical failure, general regression, consistency, cost, and latency.
The release decision weighs target lift against regression, consistency, cost, and latency, and the experiment trail behind it can be rerun by someone else.
Some of the 500+ brands we've worked with
See all references
Steps, gates, and who decides
How we work
The reason for training, the intervention itself, and the result stay connected from the first baseline comparison through the release recommendation.
Prove there is a training problem
We test the target behavior with prompt and RAG baselines, record the gap that remains, and agree the held-out tasks, critical behaviors, regression limits, budget, and decision owner.
- AI assist
- Prompt and RAG baseline results feed a model-drafted gap comparison for the product owner to challenge.
- Human gate
- Is a tuning experiment more appropriate than the simpler alternatives? Your product owner confirms the remaining gap justifies a tuning experiment.
Keep every run identifiable
Data and configuration references are fixed for each experiment. We track every run and artifact, including assumptions and changes introduced after the previous result.
- AI assist
- The model applies the agreed data-version and configuration tags to each run record, followed by a specialist's metadata check.
- Human gate
- Can every result be tied to a specific data version, configuration, and model artifact? A specialist verifies the run metadata before results are compared.
Read the lift beside the loss
Target-task improvement is compared with critical-behavior failures, general-capability regression, consistency, inference cost, and latency, split by the cases that matter.
- AI assist
- The model prepares a slice-by-slice table of target lift, regressions, and cost for specialists to verify.
- Human gate
- Do the gains survive the regression, cost, and latency checks by important slice? Your product owner decides whether the trade-off justifies the tuned model.
Put the release decision on record
The handover includes the chosen artifact, experiment trail, held-out comparison, limitations, release conditions, and the event that should trigger another review.
- AI assist
- A model-drafted release summary captures the accepted run and its documented limits for the product owner to review.
- Human gate
- Does your owner accept the evidence and the limits of the selected run? Your product owner accepts the tuned artifact and its release conditions.
Named artifacts you keep
What you get
Another specialist should be able to reproduce the run, compare it with the alternatives, and challenge the recommendation without taking the best examples on trust.
Decision record
Prompt, RAG, and tuning baseline comparison
The target behavior, prompt and RAG baseline, remaining gap, tuning rationale, acceptance checks, exclusions, and owner.
Dataset
Versioned training and evaluation manifest list
Model, data, configuration, environment, run, evaluation-set, and tuned-artifact references for each experiment.
Dashboard
Reproducible run ledger and selected-model reference
The run history, changes, failures, observations, selected artifact, and links to held-out results.
Model card
Held-out comparison report and limitations summary
Baseline and tuned results by task and critical slice, plus regressions, consistency, cost, latency, intended use, and known limits.
Scope and honest limits
When to bring us in
This engagement fits a known, repeatable behavior gap that simpler prompt and retrieval changes have already failed to close against the same baseline.
A good fit when
- Prompt and RAG baselines have run on the target tasks, but the same output behavior still falls short across held-out cases.
- Curated training data is ready, yet the separate evaluation set must still expose any critical behavior that quietly regresses.
- The experiment budget and latency needs are known, but nobody has compared the expected lift with inference cost and refresh demands.
- Prompt, RAG, and tuning options have separate results, but no baseline record shows why training is the justified intervention.
- Another specialist cannot reproduce the training runs because their data versions and configuration changes are not attached to each artifact.
- Held-out tasks show target lift, but critical slices, general regression, consistency, cost, and latency are not read beside that gain.
- A tuned artifact can be selected, yet its limitations, release conditions, owner, and retraining trigger sit in four separate places.
Better handled as other work when
- You want fine-tuning before testing a prompt or retrieval change on the same cases. The simpler baseline must establish the remaining gap first.
- Evaluation examples entered training or model selection, yet their result is being counted as lift. That leakage needs a clean comparison.
- You need production deployment or continuous retraining. Those duties require a separately scoped engagement.
If one of these is closer to your situation, start here instead: See model customization services
Engineers who ship production AI
This is the part of Zeo that writes and ships code. Our senior engineers build agents, chatbots, and RAG pipelines, along with the automation and data work around them, and they keep operating those systems once they're live. We've worked with more than 500 brands since 2011.
Tools we use
Tools behind this work
Modalprovisions on-demand GPU compute for each training run without a dedicated cluster
Weights & Biasestracks each versioned run against the prompt and RAG baselines it must beat
Braintrustscores each trained version's critical failure cases, not just its average lift
Hugging Facehosts the base model and training pipeline the fine-tuning run executes on
Unslothruns each versioned training experiment at a fraction of the usual compute cost
Predibaseserves multiple fine-tuned versions side by side for direct comparison
Adjacent work
Where this leads
Build
Fine-Tuning Dataset Curation
Supervised training runs depend on rigorously formatted, high-quality prompt-response pairs curated from domain experts.
Build
Domain Adaptation
Fine-tuning adapts foundational model weights to internal enterprise writing styles, technical nomenclatures, and strict output formats.
Assurance
Model Selection & Baseline Benchmarking
Post-training benchmark evaluations compare fine-tuned model performance directly against base model and prompt-engineered baselines.
Advisory
Fine-Tune vs RAG vs Prompting Assessment
Empirical fine-tuning results validate whether model adaptation delivers sufficient accuracy gains compared to external RAG retrieval.
Next step
Find out whether training earns its cost
Before you decide