Fine-tuning
Continuing to train a pre-trained model on a specific dataset to adapt it to a task.
What is Fine-tuning?
Pre-training an LLM costs tens of millions of dollars and weeks of GPU time. Fine-tuning takes that pre-trained model and continues training on a much smaller, task-specific dataset — making the model better at one specific job without starting from scratch.
Fine-tuning is much cheaper than pre-training but still nontrivial. Modern techniques like **LoRA** (Low-Rank Adaptation) and **PEFT** (Parameter-Efficient Fine-Tuning) make it possible to fine-tune billion-parameter models on a single GPU.
The 90% rule in Gen AI engineering: most problems can be solved with prompting + RAG, not fine-tuning. Fine-tune only when prompting fails AND you have a clear high-quality dataset AND the value justifies the operational cost.
Fine-tuning is the senior-level Gen AI skill — knowing when to use it (and when not to) is what separates effective engineers from over-engineered demos.
A Bangalore B2B SaaS company fine-tuned a Llama model on 10,000 of their own support tickets. Result: support classifications that beat GPT-4 for their specific product domain, at 1/20th the inference cost.
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