Frequently Asked Questions
Frequently Asked Questions
What is LLM fine-tuning and how is it different from using a model like ChatGPT out of the box?
Fine-tuning trains an existing model further on your proprietary data, so it learns your terminology, tone, and specific tasks instead of relying on generic internet-scale training. Off-the-shelf models like ChatGPT work fine for general use but often miss domain-specific accuracy that fine-tuning solves.
How much data do I need to fine-tune an LLM effectively?
It depends on the technique and use case, but useful results are often possible with a few thousand well-labeled examples rather than millions. We run a data audit upfront to tell you exactly how much you have and what's missing before committing to a full engagement.
How long does an LLM fine-tuning project typically take?
Most engagements take four to eight weeks from data audit to deployment, depending on data readiness and model complexity. Parameter-efficient methods like LoRA can shorten this timeline significantly compared to full fine-tuning.
Is my data safe during the fine-tuning process?
Yes, we anonymize and isolate training data before it touches any model, and deployment can happen in a private VPC or on-premise environment you control. For regulated industries, we map every step against relevant compliance standards like HIPAA, SOC 2, or GDPR.
Which foundation model should I fine-tune, GPT, Claude, LLaMA, or Mistral?
The right choice depends on your budget, data privacy needs, and specific use case, not a generic best-model ranking. We benchmark multiple models against your actual data before recommending one.
Do I need fine-tuning, or would prompt engineering or RAG solve my problem?
Not every problem needs fine-tuning, sometimes better prompting or a retrieval setup gets you there faster and cheaper. We evaluate your use case honestly and recommend fine-tuning only when it's actually the right fit.
How much does LLM fine-tuning cost?
Cost varies based on data volume, model size, and technique, with parameter-efficient methods like LoRA or QLoRA costing significantly less than full fine-tuning. We provide a scoped estimate after the initial use case and data audit, not a blanket number.
What happens after the model is deployed, does it need ongoing maintenance?
Yes, models drift as your data and user behavior evolve, so accuracy can degrade over time without retraining. We build monitoring and scheduled retraining into every deployment to keep performance from slipping months after launch.
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