Fine-Tuning
Adapting a pretrained foundation model to specific tasks or domains via additional training.
Fine-tuning is the process of adapting a pretrained foundation model — like GPT, Claude, or Llama — to a specific task, domain, or style by continuing training on curated examples. Common fine-tuning approaches include full parameter fine-tuning, LoRA (Low-Rank Adaptation), QLoRA, and instruction tuning. Fine-tuning excels when you need consistent output formatting, domain-specific vocabulary, or behaviors that prompting alone cannot reliably produce. The decision to fine-tune versus rely on prompting + RAG depends on data volume, latency requirements, and cost. Empire325 helps teams choose between prompt engineering, RAG, and fine-tuning, then implement and evaluate the chosen approach.
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Large Language Model (LLM)
A neural network trained on massive text corpora to understand and generate human language.
Retrieval-Augmented Generation (RAG)
An AI architecture combining LLM generation with real-time retrieval from external knowledge sources.
AI Agent
An autonomous LLM-based system that plans, takes actions via tools, and accomplishes multi-step goals.
Prompt Engineering
The practice of designing inputs to LLMs to elicit accurate, consistent, useful outputs.