Prompt Engineering 简明教程

Incremental Fine-Tuning − Gradually fine-tune our prompts by making small adjustments and analyzing model responses to iteratively improve performance.

Dataset Augmentation − Expand the dataset with additional examples or variations of prompts to introduce diversity and robustness during fine-tuning.

Context Window Size − Experiment with different context window sizes in multi-turn conversations to find the optimal balance between context and model capacity.

Adaptive Context Inclusion − Dynamically adapt the context length based on the model’s response to better guide its understanding of ongoing conversations.

Temperature Scaling − Adjust the temperature parameter during decoding to control the randomness of model responses. Higher values introduce more diversity, while lower values increase determinism.

Top-p Sampling (Nucleus Sampling) − Use top-p sampling to constrain the model to consider only the top probabilities for token generation, resulting in more focused and coherent responses.

Minimum Length Control − Specify a minimum length for model responses to avoid excessively short answers and encourage more informative output.

Maximum Length Control − Limit the maximum response length to avoid overly verbose or irrelevant responses.

Content Filtering − Apply content filtering to exclude specific types of responses or to ensure generated content adheres to predefined guidelines.

Language Correction − Post-process the model’s output to correct grammatical errors or improve fluency.

Reward Models − Incorporate reward models to fine-tune prompts using reinforcement learning, encouraging the generation of desired responses.

Policy Optimization − Optimize the model’s behavior using policy-based reinforcement learning to achieve more accurate and contextually appropriate responses.

Real-Time Evaluation − Monitor model performance in real-time to assess its accuracy and make prompt adjustments accordingly.

User Feedback − Collect user feedback to understand the strengths and weaknesses of the model’s responses and refine prompt design.

A/B Testing − Conduct A/B testing to compare different prompt strategies and identify the most effective ones.

Balanced Complexity − Strive for a balanced complexity level in prompts, avoiding overcomplicated instructions or excessively simple tasks.

Chatbots and Virtual Assistants − Optimize prompts for chatbots and virtual assistants to provide helpful and context-aware responses.

Content Moderation − Fine-tune prompts to ensure content generated by the model adheres to community guidelines and ethical standards.