Prompt Engineering Techniques

A qualidade das respostas de LLMs depende 80% do prompt. Domine as técnicas profissionais.

1. Chain of Thought (CoT)

Force o modelo a "pensar antes de responder":

Ruim:

```

Calcule: 234 × 567

Prompt Engineering Techniques

Prompt engineering is a crucial aspect of working with AI models. It involves crafting inputs that guide the model to produce desired outputs. This article explores various techniques and best practices.

Understanding the Basics

To effectively use prompt engineering, one must understand the model's capabilities and limitations. Here are some foundational concepts:

• Clarity: Ensure your prompts are clear and unambiguous.
• Specificity: Provide specific instructions to guide the model.

Advanced Techniques

Iterative Refinement

Iterative refinement involves adjusting prompts based on the model's responses. This technique helps in honing the output quality.

```python

Example of iterative refinement

prompt = "Translate the following text to French: 'Hello, world!'"

response = model.generate(prompt)