Spending significant tokens on system prompts trying to get the model to behave consistently?
Base model producing outputs in the wrong format or style despite detailed prompting?
LLM Fine-Tuning Services
General-purpose language models are trained to be useful to everyone. Fine-tuning makes them specifically useful to you -- adapting their behaviour, vocabulary, tone, and output format to your domain, your data, and your product requirements.
We fine-tune language models on your datasets to improve accuracy on your specific tasks, reduce prompt length and inference cost, and produce outputs that match your brand voice and format requirements without extensive prompt engineering.
Fine-tuning on OpenAI, Llama 3, Mistral, and Phi models
Domain adaptation, output format alignment, and tone calibration
Training data curation, model evaluation, and production deployment
Cost and latency analysis -- fine-tuning vs. RAG vs. prompt engineering for your use case
RaftLabs provides LLM fine-tuning services for teams that need language models adapted to their specific domain, output format, or tone requirements. We handle training data curation, fine-tuning on OpenAI (GPT-4o mini, GPT-3.5), open-source models (Llama 3, Mistral, Phi), evaluation against task-specific benchmarks, and production deployment. Fine-tuning is recommended when prompt engineering alone cannot achieve consistent output quality, when inference cost reduction is required at scale, or when domain-specific vocabulary significantly affects model performance.
When to fine-tune vs. prompt vs. RAG
Most teams reach for fine-tuning too early. The decision tree:
Try prompt engineering first. A well-structured system prompt with few-shot examples solves most output format and consistency problems without any training data.
Add RAG if the model needs your knowledge. When the model needs to answer questions about your specific documents, products, or data, retrieval-augmented generation gives it that knowledge without fine-tuning.
Fine-tune when: prompt engineering cannot achieve consistent output format despite detailed instructions, when inference cost at your expected volume makes large model usage uneconomical, or when domain-specific terminology significantly degrades base model accuracy.
We will tell you which path is right for your use case -- including if fine-tuning is not the answer.
What we do
Training data curation
The quality of your fine-tuning data determines the quality of the fine-tuned model. We help design the training data format, curate examples from your existing data sources, filter for consistency and quality, augment thin datasets with carefully generated synthetic examples, and structure prompt-completion pairs that teach the model the exact behaviour you need. Bad training data produces a confidently wrong model.
Domain adaptation
Fine-tuning a general-purpose model on your domain vocabulary, technical terminology, and document structure. Medical, legal, financial, and technical domains all have terminology patterns that general models handle poorly. Domain-adapted models produce more accurate outputs on domain-specific tasks without requiring extensive prompting to establish context on every call.
Output format alignment
When your application requires structured JSON output, specific markdown formatting, or constrained response styles that the base model ignores despite instructions, fine-tuning can reliably enforce the format. Examples include: structured data extraction in a defined schema, customer-facing responses in your brand voice and tone, classification outputs in a specific label format.
Inference cost reduction
A fine-tuned smaller model (GPT-4o mini, Llama 3 8B) can match the output quality of a larger base model on a specific task -- at significantly lower inference cost. For high-volume production applications where token cost compounds, replacing a GPT-4o production deployment with a fine-tuned smaller model for the specific task can cut inference costs by 60--80%. We benchmark the cost-quality trade-off before recommending this approach.
Open-source model fine-tuning
Fine-tuning Llama 3, Mistral, Phi-3, and Gemma models using LoRA/QLoRA adapters for deployment on your own infrastructure. Eliminates per-token costs entirely for high-volume applications. Required for air-gapped deployments or data residency requirements that prevent using hosted API providers. We handle GPU infrastructure setup, fine-tuning runs, model serving (vLLM, Ollama, or custom), and integration with your application.
Evaluation and regression testing
A fine-tuned model without an evaluation framework is a liability. We design task-specific evaluation benchmarks, run before/after comparisons, and build regression test suites that catch output degradation when models or prompts are updated. Automated evaluation pipelines that run on every model update before promotion to production. The infrastructure that makes fine-tuned model maintenance predictable.
Not sure if fine-tuning is the right path?
Tell us the use case, your current prompt approach, and where the base model falls short. We'll tell you whether fine-tuning is the answer -- or whether there's a faster fix.
Related services
Generative AI Integration -- integrating LLMs into existing applications
RAG Pipeline Development -- knowledge grounding without fine-tuning
Generative AI Consulting -- strategy and architecture before committing to a build approach
Custom AI Development -- AI-native products from scratch
Machine Learning Development -- ML systems for structured data and classification tasks
Frequently asked questions
Fine-tuning is the process of continuing to train a pre-trained language model on your specific data so it adapts to your task, domain, and output requirements. Use fine-tuning when: prompt engineering alone cannot produce consistent output format (the model keeps ignoring your instructions), when you need significant inference cost reduction at scale (a fine-tuned smaller model can outperform a larger model with a long system prompt), or when domain-specific vocabulary significantly degrades base model performance. Fine-tuning is not always the right answer -- start with prompt engineering and RAG first.
OpenAI fine-tuning API: GPT-4o mini and GPT-3.5 Turbo (hosted fine-tuning, no infrastructure required). Open-source models: Llama 3 (8B, 70B), Mistral 7B, Phi-3, and Gemma (require GPU infrastructure for training). Google Gemini fine-tuning via Vertex AI. The right model depends on: your budget (open-source eliminates per-token costs), your data privacy requirements (open-source runs on your infrastructure), and your accuracy requirements (larger models generally fine-tune to higher accuracy but cost more to run).
For OpenAI fine-tuning: 50--100 high-quality examples is the minimum; 500--1,000 is recommended for reliable improvement; 5,000+ for significant domain adaptation. Quality matters more than quantity -- 100 carefully curated examples outperform 10,000 inconsistent ones. For open-source model fine-tuning (full fine-tuning or LoRA/QLoRA adapters): 1,000--50,000 examples depending on the degree of adaptation required. We assess your existing data and help curate or generate training examples if your dataset is thin.
LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that trains a small set of adapter weights rather than the full model. It is dramatically cheaper in compute and memory than full fine-tuning while achieving comparable results for most tasks. QLoRA extends this with quantisation for even lower memory requirements. LoRA is the standard approach for fine-tuning open-source models on modest GPU infrastructure. We use LoRA/QLoRA for open-source model fine-tuning and full fine-tuning only when the task requires it.
We establish a benchmark before fine-tuning -- a representative set of inputs with expected outputs, evaluated on your task-specific metrics (accuracy, format compliance, domain terminology usage, output length consistency). The fine-tuned model is evaluated against this benchmark on a held-out test set. We only recommend proceeding to production deployment when benchmark improvement is statistically significant. Fine-tuning that does not improve over the baseline prompt-engineered base model is not worth the cost.
Fine-tuning project cost covers training data curation, fine-tuning run costs, evaluation, and deployment. For OpenAI fine-tuning (GPT-4o mini or GPT-3.5), the OpenAI training API costs are low ($1--10 for typical datasets) -- the project cost is primarily in data curation and evaluation work ($8,000--$25,000). For open-source model fine-tuning with infrastructure setup, $20,000--$60,000 including GPU compute, deployment infrastructure, and evaluation framework.