This process helps in teaching the AI model to better understand and respond to the user’s needs by starting with minimal guidance and progressively adding more detailed instructions.
By incorporating self-ask prompting, the AI can better emulate human problem-solving processes, leading to improved interaction quality and user satisfaction.
Encourages the AI to outline a strategy before diving into the task, leading to more structured and coherent responses. Helps in breaking down complex tasks into manageable steps, ensuring all aspects are covered. Results in more detailed and accurate outputs by addressing potential gaps in understanding from the outset.
Example:
The AI model is guided to articulate its reasoning process step-by-step as it works towards a solution. This approach helps the model generate more accurate and coherent responses by explicitly breaking down the problem into smaller, manageable parts and reasoning through them sequentially.
Append “explain your reasoning step by step”.
Example
ReAct is a technique that uses natural language processing (NLP) and machine learning algorithms to generate human-like responses.
This method involves the AI model breaking down a complex problem into smaller steps, reasoning about each step, and then taking actions based on its reasoning. The process iterates until the task is completed. This approach ensures that the AI model not only considers the problem logically but also adapts its actions based on intermediate results.
https://cobusgreyling.medium.com/react-synergy-between-reasoning-acting-in-llms-36fc050ae8c7
LLMs should not only be able to perform mathematical reasoning, but also symbolic reasoning which involves reasoning pertaining to colours and object types.
https://cobusgreyling.medium.com/symbolic-reasoning-pal-program-aided-large-language-models-d1910215a040
Instead of expecting a perfect answer from a single prompt, the user provides feedback or additional prompts to iteratively improve the quality and accuracy of the AI’s output. This method allows for progressive enhancement and fine-tuning of the response, ensuring it meets the desired criteria.
The AI is guided through a series of prompts in a logical sequence to progressively build a comprehensive and detailed response.
Key Differences
Ensures that the most reliable and accurate information is highlighted by cross-referencing multiple responses. Produces a final output that is coherent and internally consistent. Reduces the impact of any single erroneous or biased response by considering multiple perspectives.
Useful for Summarization, Problem-Solving and Creative Writing.
The AI will provide multiple possible responses for a single question and reason which one makes more sense.
Leverages external tools to perform accurate and reliable calculations or data retrieval. Automates complex tasks by seamlessly integrating reasoning with tool usage. Extends the functionality of AI models to handle a wider range of tasks and queries.
The user provides a task description without any examples or specific context. The AI generates a response based on its general understanding and knowledge.
The user provides a task description along with a few examples to illustrate what is expected. The AI uses the provided examples to infer the task requirements and generate a relevant response.
This method allows the AI to build a foundation of knowledge before tackling a more complex prompt, resulting in a more coherent and comprehensive response.
This approach of prompt chaining allows the AI to progressively build and refine its response, ensuring a comprehensive and detailed final output.
The user provides a broad task or problem. The AI generates multiple potential approaches or solutions, creating a “tree” of possible thoughts. The AI evaluates the different branches to determine the best course of action. The AI synthesizes the best branches into a coherent response.
Tree of Thoughts Prompting enables the AI to think critically and creatively, exploring various possibilities and combining the best ideas into a cohesive strategy. This method is particularly useful for complex problems that benefit from multi-faceted solutions.
Pull data from the internet and provide the context to the query to be able to show proper response
Automatic Prompt Engineer (APE) is an AI system designed to automatically generate, refine, and optimize prompts for other AI models. The goal of APE is to create effective prompts that maximize the performance and accuracy of the AI model’s responses. APE systems use various techniques such as machine learning, natural language processing, and feedback loops to iteratively improve prompts based on their performance.
Allows for real-time adjustments based on the AI’s responses, leading to more precise and relevant information. Each step refines the previous one, ensuring a more comprehensive and accurate output. Facilitates a more interactive and engaging conversation with the AI, making it suitable for complex or detailed inquiries.
Directs the AI to concentrate on specific aspects of a broader topic, ensuring the response aligns with the user’s interests. Encourages more detailed and in-depth responses by narrowing down the scope of the prompt. Enhances the relevance of the AI’s output to the user’s needs by providing clear guidance on the desired focus area.
An approach that integrates the capabilities of programming and computational tools with language models to enhance their performance in solving complex problems.
By leveraging programming languages or scripts, PAL can handle tasks that require precise calculations, data manipulation, or algorithmic processes, which are beyond the direct capabilities of language models alone.
User makes a question that triggers the model to write a script in Python that when executed give the answer
Reflexion is designed to help agents improve their performance by reflecting on past mistakes and incorporating that knowledge into future decisions. This makes it well-suited for tasks where the agent needs to learn through trial and error, such as decision-making, reasoning, and programming.
Helps the AI refine its responses by learning from previous outputs.
Combines different types of information (images, graphs, text) to provide a holistic analysis. Improves the AI’s ability to reason through complex problems by considering multiple data sources. Ensures that the final output is detailed, accurate, and considers various aspects of the problem.
These links were used as source of inspiration