|
Canada-QC-GATINEAU Azienda Directories
|
Azienda News:
- The Perfect Prompt: A Prompt Engineering Cheat Sheet
Define the output format (text, code, etc ) for the LLM’s response The definition of the output format tells the model how to provide the response Even better than telling is showing
- Prompt Formatting and Structure Tutorial - Google Colab
This tutorial explores various prompt formats and structural elements in prompt engineering, demonstrating their impact on AI model responses We'll use OpenAI's GPT model and the
- Gemma formatting and system instructions | Google AI for . . .
Gemma instruction-tuned (IT) models are trained with a specific formatter that annotates all instruction tuning examples with extra information, both at training and inference time The formatter has two purposes: Indicating roles in a conversation, such as the system, user, or assistant roles
- Best practices for prompt engineering with the OpenAI API
Due to the way OpenAI models are trained, there are specific prompt formats that work particularly well and lead to more useful model outputs The official prompt engineering guide by OpenAI is usually the best place to start for prompting tips
- Optimizing Prompt Formats for Large Language Models: A . . .
We compare three common formats: plain text, JSON, and HTML, using a methodology based on the “How to Read a Paper” framework Our experiments, conducted with various LLM models including GPT-3 5
- Common prompt engineering techniques all developers should master
Prompt engineering is essential for maximizing large language model (LLM) performance This blog covers eight core techniques: zero-shot prompting for basic tasks, few-shot prompting for more nuanced outputs, chain-of-thought prompting for stepwise reasoning, instruction tuning for specificity, role prompting to control tone and expertise, output formatting for structured responses, prompt
- Papers with Code - Does Prompt Formatting Have Any Impact on . . .
Experiments show that GPT-3 5-turbo's performance varies by up to 40\% in a code translation task depending on the prompt template, while larger models like GPT-4 are more robust to these variations Our analysis highlights the need to reconsider the use of fixed prompt templates, as different formats can significantly affect model performance
|
|