Prompt Engineering
📰 Lilian Weng's Blog
Prompt engineering is a method to communicate with LLMs to achieve desired outcomes without updating model weights
Action Steps
- Understand the basics of LLMs and their limitations
- Experiment with different prompt engineering methods to find what works best for a specific model
- Use heuristics to refine and optimize prompt engineering techniques
- Test and evaluate the effectiveness of prompt engineering methods on desired outcomes
Who Needs to Know This
ML researchers and engineers benefit from prompt engineering as it allows them to fine-tune LLM behavior without retraining the model, and product managers can use it to optimize language model performance in their applications
Key Insight
💡 Prompt engineering allows for flexible and efficient control of LLM behavior without updating model weights
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💡 Optimize LLM behavior without retraining using prompt engineering!
Key Takeaways
Prompt engineering is a method to communicate with LLMs to achieve desired outcomes without updating model weights
Full Article
<p><strong>Prompt Engineering</strong>, also known as <strong>In-Context Prompting</strong>, refers to methods for how to communicate with LLM to steer its behavior for desired outcomes <em>without</em> updating the model weights. It is an empirical science and the effect of prompt engineering methods can vary a lot among models, thus requiring heavy experimentation and heuristics.</p> <p>This post only focuses on prompt engineering for autoregressive language models, so nothing with Cloze tests
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