Fine-tuning

RAG is suited for knowledge-intensive scenarios requiring frequent updates, while fine-tuning is suited for scenarios that require changing the model's behavioral patterns and style adaptation.

数字分身的技术实现包括将多模态数据结构化处理、提取核心观点和决策模式存入知识库，然后通过微调或高级提示工程让大模型模拟该人的思维方式

微调过程中，数据清洗、格式标准化、指令-回答对的质量控制往往占据整个微调流程70%以上的工作量

Large model application development can be divided into three progressive levels: prompt engineering, RAG retrieval-augmented generation, and model fine-tuning.

垂直化AI通过微调、检索增强生成（RAG）或提示工程等技术将基础模型的通用能力定向适配到特定行业场景

RAG and fine-tuning are not mutually exclusive: RAG excels at dynamic, traceable knowledge queries, while fine-tuning excels at fixed styles, formats, and reasoning patterns; actual projects often adopt a combined approach of "fine-tuned model + RAG retrieval".

RAG suits knowledge bases ranging from a few documents to several hundred documents in scale; when the data volume reaches the terabyte or even petabyte level, retrieval cost and effectiveness face challenges, and fine-tuning may be more appropriate.

Diagnostic comparison table: when stable task patterns remain weak after baseline optimization, first check label quality, representative samples, model capability and held-out set results, and the candidate intervention is to evaluate fine-tuning against the baseline.

Prompting, RAG and fine-tuning are composable: a fine-tuned model can still use prompts, retrieval and tool calls.

Technology selection decisions should start from the nature of the problem: determining whether there is a knowledge gap or a behavioral mismatch, the frequency of knowledge updates, whether traceable sources are needed, whether stable formatting and tone are required, and the balance between cost and maintenance complexity.

RAG and fine-tuning address two different types of problems: one concerns what the model knows, and the other concerns how the model expresses itself and behaves.

Fine-tuning is highly inefficient at injecting factual knowledge and is better at adjusting behavioral patterns than memorizing specific facts, so knowledge gaps are more efficiently addressed with RAG.

Production-grade AI systems typically combine RAG and fine-tuning, using fine-tuning to establish stable output behavior and domain-specific language style, and using RAG to provide the most current and relevant factual grounding.

The large model engineering and deployment direction refers to application development or application training on top of existing foundation models, such as calling models like DeepSeek or OpenAI for agent development, RAG systems, or model fine-tuning.

Skills enable experts to write workflows and domain knowledge into Markdown documents, allowing others to obtain the same capabilities by downloading the folder, without the need for model fine-tuning or RAG.

Compared to fine-tuning, RAG is more lightweight and easier to update, whereas fine-tuning requires large amounts of labeled data and computing power.

Common solutions for verticalization include fine-tuning on domain-specific datasets, building dedicated RAG pipelines, and injecting domain tools into model workflows via function calling.

The typical technical approach to achieving authentic tone simulation is to have users provide samples of past posts, comments, or direct messages, with the model internalizing personal expression habits through few-shot learning or fine-tuning.

Fine-tuning is costly with long update cycles, RAG is flexible but works better for unstructured knowledge, and structured semantic layers are well-suited for highly standardized enterprise data query scenarios.

There are three main technical approaches to injecting external knowledge into large language models: fine-tuning, retrieval-augmented generation (RAG), and structured semantic layer/ontology injection.