Custom fine-tuning of language models improves accuracy, consistency, and task-specific performance in production applications. Partner AI provides end-to-end AI fine tuning services for developers and product teams. We design datasets, fine-tune models, evaluate results, and deploy improvements safely into real-world applications.
Our AI Fine Tuning Services Include:
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Fine-tuning large language models, including OpenAI and Llama for domain-specific tasks
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Instruction tuning and response optimisation
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Dataset design and preparation
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Model evaluation and performance testing
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Model distillation and fine-tuning to reduce inference cost and latency
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Prompt optimisation and caching strategies to improve efficiency at scale
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Deployment guidance and monitoring
Our Fine-Tuning Process
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Requirements & Use-Case Definition
We analyse your application, users, and desired model behaviour. -
Dataset Design & Preparation
We create and clean training datasets aligned with your domain and intent. -
Model Fine-Tuning
We fine-tune AI models using best-practice configurations. -
Evaluation & Iteration
We measure improvements in accuracy, consistency, and relevance. This can include evaluating trade-offs between model size, cost, latency, and output quality. -
Deployment & Support
We help integrate the tuned model into your production environment.
Fine-Tuning and Retrieval-Augmented Generation (RAG)
Fine-tuning and RAG solve different problems and are often used together.
Fine-tuning is best suited for shaping model behaviour — such as instruction following, response structure, tone, and task-specific reasoning. RAG is used to provide models with up-to-date or proprietary knowledge at inference time.
In many production systems, a fine-tuned model is combined with RAG to achieve both consistent behaviour and access to external knowledge. We help teams design architectures that use fine-tuning, RAG, or a combination of both depending on the application.
Who This Is For
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SaaS teams building AI-powered features
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Developers deploying AI models in production
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Companies needing consistent, domain-specific outputs
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Teams scaling beyond prompt engineering
Quality Review & Feedback Workflow
To support fine-tuning and evaluation workflows, we provide a collaborative review system that allows clients to actively participate in the fine-tuning process.
During development, clients can review model outputs, test behaviours, and provide structured feedback that is directly incorporated into dataset refinement and iterative fine-tuning. This includes optional access to a lightweight iOS interface for reviewing responses, flagging issues, and communicating feedback in near real time.
This collaborative workflow enables continuous testing, faster iteration, and tighter alignment between the fine-tuned model and the application’s requirements throughout the project.
Talk to an AI Engineer
Discuss your use case and determine whether fine-tuning is the right approach. Download the Partner AI App to start a chat with our AI Team. Alternatively, use the contact form on this website.