Generative AI · Custom LLM Training
LLM Fine-Tuning Services
LLM fine-tuning trains an existing language model on your examples so it follows your format, tone or task more reliably, often in a smaller, cheaper model. Aaga tells you first whether you need it at all, then handles data preparation, training, evaluation and deployment when the numbers say fine-tuning will pay off.
- A clear answer on whether prompting, RAG or fine-tuning fits your problem
- Training data prepared, labeled and checked for quality and privacy
- Smaller fine-tuned models for high-volume tasks at lower cost and latency
- Measured against the baseline before anything reaches production
- Prompting and RAG are tested before any training
- Baseline first
- Open-weight models or provider fine-tuning APIs
- Open or managed
- Reply to your inquiry within one business day
- 1 day
When Does Fine-Tuning Beat Prompting and RAG?
Many teams don't need fine-tuning. Use this table to see which approach to try first for your situation.
| Your situation | Try first | Why |
|---|---|---|
| Answers depend on your documents or facts that change | RAG | Fine-tuning doesn't reliably add facts and can't cite sources or respect access rights |
| Only a handful of examples of the desired output | Prompting with few-shot examples | Too little data to train on; good examples in the prompt often close the gap |
| Output must follow a strict schema | Structured outputs and validation | Many current model APIs can constrain output to a JSON schema; fine-tune only if errors persist |
| Narrow task at very high volume | Fine-tune or distill a small model | A small tuned model can match a large one on one task at lower cost and latency |
| Specialized labels, jargon or house style that prompts can't capture | Fine-tuning | Hundreds to thousands of good examples teach patterns that are hard to describe |
| Data must stay in your environment | Self-hosted open-weight model | Fine-tune it only if the base model falls short on your evaluation set |
Fine-tuning and RAG are often combined: RAG supplies the facts, and a tuned model handles format and style.
What Do Our LLM Fine-Tuning Services Include?
Fine-tuning succeeds or fails on data and evaluation. Training runs are the shortest part of the project.
Data Preparation and Labeling
Collect, clean, deduplicate and label examples from your logs, tickets and documents, with guidelines and reviewer agreement checks.
Synthetic Data Generation
Generate additional examples for rare cases with a stronger model, then filter and review them so quality doesn't drift.
LoRA and QLoRA Fine-Tuning
Parameter-efficient training that updates small adapter weights, so open-weight models can be tuned on modest GPUs and swapped per task.
Distillation Into Smaller Models
Train a compact model on outputs from a large one for a single task, cutting cost per request and latency at high volume.
Evaluation and Red-Teaming
Held-out test sets, comparison against the prompted baseline, regression checks on general ability and adversarial testing for unsafe behavior.
Deployment and Serving
Serve tuned models with vLLM in your cloud, or on managed endpoints from AWS, Azure or Google Cloud, with monitoring and rollback.
From Baseline to a Fine-Tuned Model in Production
Define the Task and Metric
We agree on the exact task, what a good output looks like and how it will be scored.
Measure the Baseline
We test strong prompting, structured outputs and RAG first. If they meet the target, you skip training.
Prepare the Dataset
Training, validation and test splits from your data, with PII removed and labels reviewed.
Train and Compare
LoRA, QLoRA or managed fine-tuning runs, each scored against the baseline on the same held-out test set.
Deploy and Monitor
Serve the winning model, watch quality and drift in production, and retrain when your data changes.
How Do We Protect Your Training Data?
Training data often contains customer conversations, internal documents or personal data, so privacy is designed in from the first step.
- Minimize and redact. We remove or mask personal data, credentials and secrets before training, and keep only the fields the task needs.
- Train where your data lives. Open-weight models can be fine-tuned and served entirely in your own cloud account or data center.
- Check provider terms. For managed fine-tuning, we confirm how the provider stores training data and the resulting model, and in which regions.
- Respect licenses. Open-weight model licenses and some providers' terms restrict how models and outputs may be used, including for distillation. We check them before we start.
- You get the artifacts. Datasets, adapters, evaluation sets and training code are handed over, with ownership agreed in the contract.
What Is LLM Fine-Tuning?
LLM fine-tuning is the process of taking a pretrained large language model and training it further on examples of the inputs and outputs you want. The model's weights, or a small set of adapter weights, change so it follows your task, format and style more consistently without long instructions in every prompt. Custom LLM training usually means this kind of fine-tuning on an existing model, not training a new model from scratch, which few businesses need.
Aaga offers fine-tuning as part of our generative AI development work. We start every engagement by checking whether you need it.
Why Many Teams Don't Need Fine-Tuning
Fine-tuning is often the first idea and rarely the first step. Today's models follow detailed instructions well, can be constrained to a JSON schema, and can read your documents at query time through retrieval. Before training anything, we test three cheaper options on your data:
- Better prompts with clear instructions and a few high-quality examples.
- Structured outputs with validation and automatic retries.
- Retrieval-augmented generation for anything that depends on your documents. See our RAG development services.
If these meet the target on your evaluation set, you save the cost of training, hosting and retraining a custom model. If they don't, you now have a baseline to beat and a clear picture of the gap.
When Fine-Tuning Pays Off
Fine-tuning earns its place in a few well-defined situations:
- High volume, narrow task. Classifying or extracting from very large numbers of documents or messages, where a small tuned model is much cheaper per request than a frontier model.
- Latency-sensitive features. In-product suggestions or real-time voice where a smaller model responds faster.
- Hard-to-describe patterns. House style, domain jargon or labeling decisions that experts recognize but can't fully write down.
- Private deployment. An open-weight model running in your environment that needs help to reach the quality of hosted models on your task.
How We Prepare Training Data
Data quality decides the outcome. We extract candidate examples from your logs, tickets, documents or expert reviews, remove duplicates and personal data, and write labeling guidelines. Domain experts review a sample, and we measure agreement between reviewers before scaling up. Where real examples are scarce, our dataset collection and synthetic data creation teams fill the gaps, with filtering so generated examples don't teach the model bad habits.
Training Methods We Use
- Supervised fine-tuning (SFT) on input and output pairs, the most common starting point.
- LoRA and QLoRA for parameter-efficient tuning of open-weight models, using tools such as Hugging Face TRL and PEFT.
- Preference tuning such as DPO, when you have examples of better and worse answers.
- Distillation from a large model to a small one for a single task.
- Managed fine-tuning offered by model and cloud providers, for supported models, when you prefer not to manage GPUs.
How We Evaluate a Fine-Tuned Model
Every model is scored on a held-out test set it never saw in training, side by side with the prompted baseline. We check task accuracy, format compliance, latency and cost per request, and run regression tests so the model hasn't lost general abilities it needs. Red-teaming probes for unsafe outputs, leaked training data and prompt-injection weaknesses before release. After launch, monitoring flags drift so you know when to retrain.
What Drives the Cost of a Fine-Tuned Model?
The training run is rarely the largest cost. Plan for these drivers:
- Data preparation. Collecting, cleaning and reviewing examples usually takes the most expert time.
- Training compute. LoRA and QLoRA need far less GPU time than full fine-tuning, and managed services charge per training job or per token.
- Hosting. A self-hosted model needs GPUs running whether or not traffic arrives; managed endpoints charge per use or per provisioned capacity. vLLM can serve several LoRA adapters on one base model, which keeps multi-task hosting efficient.
- Upkeep. New base models, changing data and drift mean periodic retraining and re-evaluation.
We estimate these against the baseline's running cost, so the decision to fine-tune rests on numbers from your own workload.
Why Aaga for Fine-Tuning
Aaga has worked with 100+ clients across the USA, Canada, the UK, the Netherlands, Dubai (UAE) and India. You work directly with senior engineers who will tell you when not to train a model. We are model-agnostic, start with a scoped assessment and hand over every dataset, adapter and evaluation set. Fine-tuned models often power a wider product, such as an internal AI copilot. Contact us to discuss your task.
Frequently Asked Questions
LLM fine-tuning services adapt an existing large language model to a specific task by training it further on your examples. The work includes deciding whether fine-tuning is needed, preparing and labeling data, running training, evaluating the result against a baseline and deploying the model securely.
Often not. Current models handle many business tasks well with good prompts, structured outputs and RAG over your documents. Fine-tuning makes sense for narrow, high-volume tasks, specialized output styles or labels, or when you need a smaller model to cut cost and latency. We test the simpler options first.
Not reliably. Fine-tuning is good at teaching behavior, format and style, but it is a poor way to store facts: they can't be updated easily, cited or restricted by user. Use RAG for knowledge, and fine-tuning for how the model responds.
LoRA freezes the base model and trains small adapter matrices, which makes fine-tuning much cheaper than updating every weight. QLoRA does the same on a base model quantized to 4-bit precision, which reduces GPU memory further so larger models can be tuned on smaller hardware.
It depends on the task. A narrow format or classification task may improve with a few hundred high-quality examples, while harder tasks need thousands. Quality and consistency matter more than volume, so we usually start with a smaller, carefully reviewed set and add data where evaluation shows gaps.
Distillation trains a smaller model to reproduce the outputs of a larger one on a specific task. Done well, the small model gets close to the large model's quality on that task while costing less per request and responding faster. Model licenses and provider terms must allow using the larger model's outputs this way.
Open-weight models can be served with vLLM or similar inference servers in your own cloud or data center, including several LoRA adapters on one base model. Models tuned through a provider's fine-tuning service run on that provider's managed endpoints. We recommend based on privacy, volume and cost.
Find Out If Fine-Tuning Is Worth It
Share the task and a few examples. We'll measure the baseline and tell you honestly whether training a custom model will pay off.
Get a Fine-Tuning Assessment