AI TRAINING
Fine-Tuning LLMs: When, How, and Why
Decide confidently whether to fine-tune, prompt, or use RAG — then execute it correctly.
What it covers
Participants work through a structured decision framework comparing prompting, retrieval-augmented generation, and fine-tuning across cost, latency, and quality dimensions. The programme covers dataset curation, instruction-tuning formats, LoRA/QLoRA techniques, evaluation design, and deployment cost modelling. Hands-on labs use open-source tooling (Hugging Face, Axolotl, LM Evaluation Harness) on realistic domain datasets. By the end, teams can confidently scope, execute, and evaluate a fine-tuning project in their own infrastructure.
What you'll be able to do
- Apply a structured decision tree to determine whether prompting, RAG, or fine-tuning is the right approach for a given use case
- Curate and format a domain-specific instruction dataset suitable for supervised fine-tuning
- Run a QLoRA fine-tuning job on an open-source model using Hugging Face TRL or Axolotl
- Design and execute an evaluation suite combining automated metrics and LLM-as-judge scoring
- Estimate total cost of ownership (GPU compute, storage, inference) for a fine-tuned model vs hosted API alternatives
Topics covered
- Prompting vs RAG vs fine-tuning: a cost-quality-latency decision tree
- Dataset curation, cleaning, and instruction-format design (JSONL, ShareGPT, Alpaca)
- Full fine-tuning vs parameter-efficient methods: LoRA, QLoRA, prefix-tuning
- Supervised fine-tuning (SFT) and RLHF/DPO alignment techniques
- Evaluation frameworks: BLEU, ROUGE, LLM-as-judge, domain-specific benchmarks
- Tooling selection: Hugging Face TRL, Axolotl, LLaMA-Factory, OpenAI fine-tune API
- Infrastructure and cost modelling: GPU hours, cloud vs on-prem, quantisation tradeoffs
- Deployment and monitoring of fine-tuned models in production
Delivery
Delivered over 2–3 days, either in-person or fully remote via video conferencing with shared cloud GPU environments (e.g., Lambda Labs, RunPod, or AWS). Approximately 60% hands-on labs, 40% instruction and discussion. Participants receive a pre-configured notebook repository and retain access to lab materials post-training. A short async pre-work module (2–3 hours) on transformer fundamentals is recommended for mixed-level cohorts.
What makes it work
- Define a measurable evaluation benchmark before writing a single training example
- Start with the smallest model that meets quality requirements to minimise compute cost
- Invest heavily in dataset quality and diversity — model behaviour reflects data behaviour
- Track experiments rigorously (Weights & Biases, MLflow) to enable reproducibility and regression detection
Common mistakes
- Fine-tuning when a well-crafted system prompt or RAG pipeline would solve the problem at a fraction of the cost
- Using too little or poorly cleaned training data, producing a model that overfits or degrades on out-of-distribution inputs
- Neglecting evaluation design before training — leading to no reliable signal on whether the fine-tune actually improved the model
- Ignoring inference cost and latency implications of larger fine-tuned models compared to smaller prompted alternatives
When NOT to take this
A team that has never shipped an LLM-powered feature to production and is jumping straight to fine-tuning to avoid prompt engineering work — they should first validate the use case with prompting before incurring fine-tuning complexity and cost.
Providers to consider
Sources
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