Simplified Approach for advanced prompt engineering techniques

Commonly used advanced prompt engineering techniques, explained using simple human analogies! Although there is an influx of prompt engineering papers, most of them are straightforward and closely mirror the methods humans use to solve problems. By applying analogies from the human thought process, you can swiftly grasp their concepts. These can be categorized into the following:

commonly used advanced prompt engineering techniques explained using simple human analogies:

1. Chaining Methods

• Analogy: Solving a problem step-by-step, like following a recipe.
• Examples: Zero-shot/Few-shot CoT (Completion of Thought), where the model completes a thought based on a few initial words.

2. Decomposition Based Methods

• Analogy: Breaking a complex problem into smaller, more manageable parts.
• Examples: Least-to-most prompting, where the model is given progressively more information to complete a task, and Question Decomposition, where a complex question is broken down into simpler sub-questions.

3. Path Aggregation Methods

• Analogy: Generating multiple options to solve a problem and choosing the best one, similar to brainstorming different solutions.
• Examples: Graph of Thoughts, where the model considers multiple paths of reasoning before generating an output, and Tree of Thoughts, which explores different branches of thinking before reaching a conclusion.

4. Reasoning Based Methods

• Analogy: For each sub-task, reasoning and verifying if they were performed correctly, akin to checking each step of a process.
• Examples: CoVe (Consistency Verification), where the model verifies the consistency of its responses, and Self-Consistency, where the model checks its own outputs for consistency.

5. External Knowledge Methods

• Analogy: Using external tools and knowledge to complete a task, like consulting a reference book.
• Examples: CoK (Consistency of Knowledge), which ensures that the model’s outputs are consistent with external knowledge sources, and ART (Adaptive Response with Knowledge Transfer), where the model leverages external knowledge for better performance.

Conclusion: Better Understanding the above mentioned analogies can help in grasping the concepts behind advanced prompt engineering techniques more easily.