Topic
Fine-Tuning Fundamentals
9 posts on fine-tuning fundamentals — part of glossary on the n4n AI blog.
What is fine-tuning? A plain-English explanation
A practitioner's guide to fine-tuning LLMs — what it actually does, how it differs from prompting and RAG, when to use it, and the traps that waste engineering time.
LoRA explained: fine-tuning LLMs efficiently
LoRA fine-tuning explained: low-rank adaptation mechanics, rank selection, memory savings, and practical trade-offs for engineers shipping LLM customization.
How to fine-tune GPT-4o with OpenAI's API
A step-by-step guide to fine-tuning GPT-4o with OpenAI's API, covering data preparation, job creation, monitoring, and deployment.
How much does it cost to fine-tune a model
A practical breakdown of the cost to fine-tune a model, covering GPU pricing, data prep, eval, and hidden expenses with real numbers and code.
How much data do you need to fine-tune a model
A practical guide to estimating training data requirements for LLM fine-tuning, with rules of thumb, quality thresholds, and evaluation strategies.
Full fine-tuning vs parameter-efficient fine-tuning
A head-to-head comparison of full fine-tuning vs PEFT across compute, capabilities, ergonomics, and limits — with a verdict for every use case.
Fine-tuning vs pretraining: what actually changes
A practitioner's breakdown of fine-tuning vs pretraining across cost, latency, capabilities, and operational reality — with a decision framework for your use case.
Fine-tuning Llama 3: what you need to know
A practical guide to fine-tuning Llama 3 models — data prep, LoRA config, training loops, and deployment tradeoffs for engineers building production systems.
Common mistakes when fine-tuning an LLM
Avoid the most common fine-tuning mistakes that waste compute, degrade performance, and produce models that fail in production.
More topics in glossary
- Structured Outputs & JSON Mode19
- AI Agents Fundamentals12
- Hallucination in LLMs11
- Sampling Parameters: Top-p, Top-k & Penalties11
- Context Window & Context Length10
- Foundation Models: Base vs Instruct vs Chat9
- Model Families & Naming Conventions: GPT-5, Claude, Gemini 3, Llama 4, Mistral, DeepSeek, Qwen, Grok9
- Grounding & Fact-Checking in AI8
- LLM Benchmarks: MMLU, HumanEval, SWE-bench & GPQA8
- Max Tokens, Stop Sequences & Output Truncation8
- Quantization Formats: GGUF, GPTQ, AWQ & INT4/INT88
- Chain-of-Thought Prompting7