Artificial Intelligence (AI) models are becoming more sophisticated, and efficient communication with these models is crucial. Various prompt engineering strategies have been developed to facilitate this communication, utilizing concepts and structures similar to human problem-solving methods. These strategies can be categorized into different types: chaining methods, decomposition-based methods, path aggregation methods, reasoning-based methods, and external knowledge methods.
Chaining methods, such as Zero-shot and Few-shot chain-of-thought (CoT) prompting, involve directing AI models through a series of steps, similar to how humans solve problems one step at a time. Zero-shot CoT prompting requires the AI to generate a logical sequence of steps to solve a problem, without any prior examples. Few-shot CoT prompting, on the other hand, involves providing the AI with a few input-output examples, enabling it to discover patterns and apply its logic to unique situations.
Decomposition-based methods, such as Least-to-Most Prompting and Question Decomposition, are akin to breaking down complex problems into smaller, more manageable sub-problems. The former deals with problems from simple to complex, assisting in the solving of subsequent sub-problems. The latter decomposes difficult questions into simpler subquestions to improve the accuracy and reliability of the model’s reasoning.
Path aggregation methods, similar to brainstorming sessions, generate several solutions and choose the best one. Models such as the Graph of Thoughts (GoT) and Tree of Thoughts (ToT) implement this approach. The GoT models data as a graph, linking different thoughts to produce better results. ToT maintains a hierarchical tree of thoughts, allowing the model to assess its progress and search for answers methodically.
Reasoning-based methods focus on verifying the accuracy of solutions. Techniques like Chain of Verification (CoVe) and Self-Consistency fall into this categorization. CoVe uses methodical questioning to assess the accuracy of preliminary solutions and correct errors, while self-consistency involves asking the model the same question multiple times and using the most common response.
Lastly, external knowledge methods, similar to how humans use external tools, enable the AI to access additional data or resources. The Consortium of Knowledge (CoK) and Automatic Reasoning and Tool-use (ART) are examples of such methods. CoK uses a retrieval tool to provide AI with relevant material, while ART combines the use of external tools with multistep reasoning to solve tasks.
As AI continues to evolve and its functionality grows increasingly complex, these advanced prompt engineering techniques and their analogies to human problem-solving methods will become even more essential in facilitating efficient and effective communication between humans and AI.
