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Services/Model Finetuning & Optimization

Model Finetuning & Optimization

We improve model performance for your specific use case through fine-tuning, prompt engineering, RAG optimization, and evaluation frameworks.

Fine-TuningOpenAILoRAPEFTEvaluationDataset Curation

General-purpose models give general-purpose results. When your use case demands consistent, domain-specific accuracy — legal document analysis, clinical note extraction, sales persona simulation, technical support classification — a fine-tuned model will outperform a prompted base model and cost less to run.

We design and run complete fine-tuning pipelines: from dataset audit and curation through training, evaluation, and production deployment. We also build the evaluation infrastructure that tells you whether the model is actually better — before it goes live.

What We Deliver

Dataset Curation and Annotation Pipelines

Fine-tuning quality is constrained by data quality. We start with a systematic audit of your existing data, identify gaps, design annotation guidelines, and build tooling to scale high-quality labelling. For domain-specific tasks where ground truth is expensive, we design active learning workflows that prioritise the highest-value examples to annotate.

Typical dataset sizes for effective fine-tuning: 500–2,000 high-quality examples for task-specific adaptation; 5,000–50,000 for broader behavioural alignment.

Fine-Tuning Pipelines

End-to-end training pipelines for:

  • OpenAI fine-tuning (GPT-4o, GPT-4o-mini) — best for teams already using the OpenAI API who want consistent output format, improved task accuracy, or reduced prompt length
  • LoRA and QLoRA for open-source models (Llama 3, Mistral, Phi-3, Qwen) — preferred when inference cost, data privacy, or on-premise deployment requirements rule out managed APIs
  • Full fine-tuning on A100/H100 clusters for cases requiring deep behavioural change

We handle hyperparameter optimisation, training monitoring, checkpoint management, and infrastructure provisioning — the full pipeline, not just a training script.

Automated Evaluation Frameworks

A model is only better if you can measure that it's better. We build evaluation frameworks with:

  • Held-out test sets with annotated ground truth
  • Automated scoring (exact match, F1, LLM-as-judge, task-specific metrics)
  • Regression suites that run on every model version before promotion
  • Human evaluation panels for subjective quality dimensions (tone, accuracy, helpfulness)

Without a rigorous evaluation framework, you can't tell whether you've improved performance or just overfit to a limited test set.

Prompt Optimisation Alongside Fine-Tuning

Fine-tuning and prompt engineering are complementary, not alternatives. We run structured prompt optimisation in parallel — systematically testing prompt variations against your evaluation set to find the highest-performing combination of prompts and model weights.

LLMOps for Fine-Tuned Models

Production deployment of fine-tuned models with: model versioning and promotion gates, canary deployment for gradual traffic shifting, continuous evaluation to catch drift, and cost monitoring. Fine-tuning without a deployment pipeline means you can't safely iterate.

When to Fine-Tune

Fine-tuning is the right choice when:

  • Prompt engineering alone isn't getting consistent results — you've iterated extensively on prompts but accuracy on your domain-specific task is still below acceptable thresholds
  • Output format consistency is critical — you need the model to reliably produce structured outputs (JSON, specific classifications, constrained responses) without complex parsing logic
  • Inference cost is a constraint — a fine-tuned smaller model (GPT-4o-mini, Llama 3 8B) can match the accuracy of a prompted larger model at a fraction of the cost
  • Data privacy requires on-premise hosting — you can't send sensitive data to managed APIs; fine-tuned open-source models running in your own infrastructure are the answer
  • You have proprietary domain knowledge that public models lack — clinical terminology, legal concepts, internal product knowledge, industry-specific jargon

Related Work

We fine-tuned a custom LLM for a voice roleplay platform, using a dataset of 8,000+ annotated sales conversations. The fine-tuned model achieved a 35% improvement in persona consistency over the base GPT-4 model, measured by a human evaluation panel. Prompt engineering time was eliminated entirely. The model now handles 2,000+ sessions per week in production.

Frequently Asked Questions

For task-specific fine-tuning (output format, classification, constrained generation): 500–2,000 high-quality examples is often sufficient. For broader behavioural alignment or knowledge injection: 5,000–50,000. The quality of examples matters far more than the quantity — 500 carefully curated examples outperform 5,000 noisy ones.
Different tools for different problems. RAG is better for dynamic knowledge that changes frequently or needs sourcing/citation — it retrieves current information at inference time. Fine-tuning is better for consistent task behaviour, output format, tone, and domain-specific accuracy on stable knowledge. Many production systems use both: fine-tuned model + RAG retrieval.
A focused fine-tuning engagement — dataset curation, training, evaluation, deployment — typically takes 4–8 weeks. The largest time investment is data curation if you don't have clean annotated data. Training itself is fast; evaluation framework development and iteration take more time than teams expect.
Catastrophic forgetting is a real risk with aggressive full fine-tuning. We mitigate it through LoRA (which adds task-specific parameters without overwriting base weights), careful dataset design that includes general examples alongside domain-specific ones, and evaluation that explicitly tests for capability regression.
Significant. A fine-tuned GPT-4o-mini model typically costs 10–20× less per token than GPT-4o, and can match GPT-4o's accuracy on specific tasks it's been trained for. For high-volume production workloads, this compounds to meaningful savings — often $10,000–$100,000+ per month at scale.

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