Advanced Prompt Engineering Techniques
Based on the presentation by Greg DeCarlo, here are the key prompt engineering techniques:
- In-Context Learning
- Keywords: Emergent abilities, temporary learning, meta-learning
- Utilize the model’s ability to learn from prompts temporarily
- Adapt prompts based on the specific context of your project
- Zero-Shot and Few-Shot Prompting
- Keywords: Task description, example-based learning
- Zero-Shot: Provide clear task descriptions without examples
- Few-Shot: Include 2-3 relevant examples before the main task
- Chain-of-Thought (CoT) Prompting
- Keywords: Step-by-step reasoning, intermediate steps, logical thinking
- Guide the model to break down complex problems into smaller steps
- Useful for tasks requiring multi-step solutions or logical reasoning
- Chain-of-Symbol (CoS) Prompting
- Keywords: Spatial reasoning, symbol interpretation, text formatting
- Use random symbols to assist with spatial reasoning in text
- Helpful for tasks involving layout or structure interpretation
- Self-Consistency Decoding
- Keywords: Multiple rollouts, consistency check, reliability
- Generate multiple CoT solutions and select the most common conclusion
- Improves reliability for complex reasoning tasks
- Generated Knowledge Prompting
- Keywords: Fact generation, contextual information, commonsense reasoning
- First prompt the model to generate relevant facts, then use these facts in the main prompt
- Enhances performance on tasks requiring specific knowledge or context
- Prompt Chaining
- Keywords: Sequential prompts, task breakdown, structured responses
- Combine multiple prompts in sequence to guide the model through complex tasks
- Useful for breaking down projects into manageable steps
- Tree of Thoughts (ToT)
- Keywords: Multiple paths, evaluation, breadth-first search, beam search
- Generate and evaluate multiple possible next steps for complex problem-solving
- Effective for tasks requiring exploration of different solution paths
- Maieutic Prompting
- Keywords: Recursive explanations, logical consistency, self-questioning
- Prompt the model to explain parts of its explanation recursively
- Improves performance on tasks requiring high logical consistency
- Least-to-Most Prompting
- Keywords: Sub-problem listing, sequential solving, task decomposition
- Guide the model to list sub-problems and solve them in sequence
- Ideal for breaking down complex challenges into smaller, manageable tasks
- Complexity-Based Prompting
- Keywords: Multiple rollouts, longest chains, thoughtful reasoning
- Generate multiple CoT solutions and select based on complexity and consensus
- Useful for tasks requiring deep, multi-step reasoning
- Self-Refinement
- Keywords: Iterative improvement, self-critique, feedback loop
- Prompt the model to solve, critique, and refine its own solutions
- Valuable for iteratively improving project outcomes during the course of the project
More Tips:
- Experiment with combining these techniques based on your specific project needs
- Use CoT and ToT for complex problem-solving and reasoning tasks
- Leverage Few-Shot prompting when you have clear examples of desired outputs
- Apply Self-Consistency and Complexity-Based Prompting for increased reliability in critical components
- Utilize Self-Refinement for iterative improvement of your project