技术方法

Prompt Engineering

定义

The practice of designing and optimising input prompts to elicit the best results from AI models, especially LLMs. Structured prompting—using system instructions, examples, and constraints—can dramatically improve output quality without changing the underlying model.

相关术语

Large Language Model (LLM)

AI models trained on vast amounts of text data that can understand and generate human-like text. Examples include GPT-4, Claude, and Llama. LLMs power modern chatbots, content generation, and code assistance tools.

Fine-tuning

The process of taking a pre-trained AI model and further training it on a specific dataset to adapt it for a particular task or domain. Fine-tuning can deliver significantly better performance than prompting alone for specialised enterprise workflows.

Few-Shot Learning

A prompting or training approach where a model learns to perform a new task from only a small number of labelled examples. In-context few-shot prompting lets practitioners shape LLM behaviour without any weight updates, dramatically lowering the cost of customisation.

Chain-of-Thought (CoT)

A prompting technique that encourages an LLM to articulate its reasoning step by step before producing a final answer. CoT significantly improves performance on multi-step reasoning, mathematics, and complex decision-making tasks.

了解术语只是第一步，将其落地应用才是第二步。

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Prompt Engineering

定义

相关术语

Large Language Model (LLM)

Fine-tuning

Few-Shot Learning

Chain-of-Thought (CoT)

相关服务

了解术语只是第一步，将其落地应用才是第二步。

Prompt Engineering

定义

相关术语

Large Language Model (LLM)

Fine-tuning

Few-Shot Learning

Chain-of-Thought (CoT)

相关服务

了解术语只是第一步，将其落地应用才是第二步。