20 important tasks for a prompt engineer in AI:
- Task Definition: Clearly defining the task or tasks the AI model needs to perform.
- Prompt Design: Crafting effective prompts that convey the task to the model.
- Domain Understanding: Developing a deep understanding of the domain or domains relevant to the task.
- Data Analysis: Analyzing relevant data to inform prompt design and model training.
- Model Selection: Choosing the appropriate pre-trained model architecture for the task.
- Hyperparameter Tuning: Optimizing hyperparameters to improve model performance.
- Prompt Testing: Experimenting with different prompts to evaluate their effectiveness.
- Response Analysis: Analyzing model-generated responses to assess quality and relevance.
- Error Analysis: Identifying and understanding common errors made by the model.
- Fine-tuning Strategies: Developing strategies for fine-tuning models based on prompt performance.
- Benchmarking: Comparing model performance against benchmarks and competitors.
- Ethical Considerations: Considering ethical implications of prompt design and model behavior.
- Bias Mitigation: Implementing techniques to mitigate bias in model outputs.
- Robustness Testing: Testing models for robustness against adversarial inputs and edge cases.
- Scaling Strategies: Developing strategies for scaling prompt engineering processes.
- Documentation: Documenting prompt design decisions and model behavior for reproducibility.
- Collaboration: Collaborating with domain experts, researchers, and stakeholders.
- Feedback Incorporation: Incorporating feedback from users and stakeholders to improve prompts.
- Continual Learning: Staying updated on advancements in AI and prompt engineering techniques.
- Communication: Effectively communicating findings and recommendations to stakeholders and team members.
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