Dry goods: ten realms of dialogue with ChatGPT artificial intelligence

Recently, I saw a great god on Reddits summing up the ten realms of prompt word cultivation, and I felt too incisive.

Usually using chatGPT or perplexiity every day, artificial intelligence really greatly promotes the efficiency of work. Although it is used a lot, it is still very shameful to compare these ten chromatography, and at most it is only to the sixth layer.

No matter how powerful artificial intelligence is, it is also a tool, so the people who can make better use of this tool will be the ones who walk at the peak of the next trend.

Master Hint Project: Advance through the ten levels of Prompt and ChatGPT

Tier 1: Basic prompts, which involve basic open-ended questions.

These prompts are straightforward, invite broad answers, and usually don't require context. They are demanding on the interpretation of AI.

For example: What is photosynthesis?

Tier 2: Basic tips for specific guidance to help narrow down the AI's answers.

These tips are simple, but contain a layer of specificity. They guide AI by outlining what is expected to respond.

For example: Can you explain photosynthesis at a level appropriate for a fifth-grader?

Tier 3: Intermediate prompts, which require AI to interpret and process user input.

These prompts require AI to interpret user input and generate appropriate answers. They require AI to have some understanding and adaptability.

For example: Can you translate the English phrase "See you tomorrow" into French?

Tier 4: Complex prompts, where AI handles multi-layered tasks.

These prompts require AI to handle multiple tasks simultaneously. AI is expected to be able to classify and prioritize different user inputs or targets.

For example: summarizing the plot of "To Kill a Mockingbird" and analyzing its theme.

Tier 5: Advanced zero-sample prompting, AI analyzes complex instructions without any output examples.

These prompts require complex and multi-layered instructions, but do not have any output examples (this is known as zero-sample prompts). AI is expected to be able to analyze complex user input and generate responses accordingly.

For example: Provide a comprehensive five-year financial plan based on: annual income of $80,000, annual expenditure of $45,000, investments: stocks and bonds totaling $100,000, debt: mortgage loans of $150,000.

Tier 6: Advanced prompts with context to guide AI using external context.

These prompts ask users to supplement external sources and contextual text to further guide AI in the direction the user expects.

Context for copy-pasted blocks of text, data, or anything else from external sources) + user-requested tasks (general tips) + guidance for using context to perform tasks

Tier 7: Conversational prompts guide AI to communicate according to a pre-structured dialogue process.

Prompts are designed to guide the conversation in a predetermined direction. They are crafted so that the AI can discuss along a planned route.

Now enter the "Social Media Post Generation Mode", which limits your input and output within a predefined framework designed to create engaging social media content. After each user command, the [Help] option for the next step is provided in a list. Generate imaginative, engaging, concise, and tailored prompts for your social media audience.

• Step 1: In order to initiate the process, confirm that you understand and are ready to enter the "Social Media Post Generation Mode". If you're ready, reply "Social media post generation mode is ready." If not, reply "Error: I can't follow these instructions".

• Step 2: The command for the user to activate the "Social Media Post Generation Mode" is [Start SMPGM]. Please reply "SMPGM is activated, waiting for your theme" and start accepting predefined input. Stay in "Social Media Post Generation Mode" until you receive the [End SMPGM] command.

• Step 3: The first command under "Social Media Post Generation Mode" should be [Subject], followed by a short description of the desired social media post topic. If the input does not meet the requirements, reply with [Input error] or [Grammatical error].

• Step 4: Based on the input provided in step three, generate three possible social media post prompts that are concise, creative, and engaging with your social media audience. Suppose the prompts are arranged by numbers 1, 2, 3. Users can indicate their preferred prompt by issuing the [pX] command, where X is the number of the selected prompt.

• Step 5: When users wish to get three new prompts based on the previous [subject], they should use the [next] command. If the user wishes to start with a new [theme], they can issue the [theme] command.

• Step 6: If the user wants to provide feedback on the generated prompt, they can use the [good] or [bad] command. If the user wishes to modify the generated prompt, they can issue the [change] command and make suggestions.

• Step 7: To end the "Social Media Post Generation Mode", the user should issue the [End SMPGM] command. Reply with a confirmation message stating that the "Social Media Post Generation Mode" has ended. For any mismatched input or command syntax, reply immediately with [Input Error] or [Syntax Error]. The user can issue the [help] command to get a list of all predefined inputs and outputs. Remember, the goal is to generate prompts that are engaging, concise, and appropriate for social media.

• Instructions for AI chatbots: AI, switch to "Social Media Post Generation Mode" ([SMPGM]) and limit input/output to the list of commands here. Start [SMPGM], end [End SMPGM]. Preset inputs include: [Start SMPGM], [End SMPGM], [Subject], [pX], [Next], [Bad], [Good], [Change], [Help]. If you have any concerns, feel free to ask, otherwise, let's get started. ”

Tier 8: Quick links, a context-based, interrelated hinting technique.

Prompt chaining involves using the output of one prompt as the input to the next, creating a "chained" prompt. This technique enhances task automation and generates responses based on incremental and contextual information.

Example: Tip 1: "What are the ingredients for lasagna in Italy?" (The output of tip 1 gives the ingredients), and tip 2: "What are the steps to make lasagna Italy using these (list inserted from the output of tip 1) ingredients?" (recipes are given). Obviously, this applies to more advanced tasks.

Tier 9: Use high-level context-led hints with sample output for more accurate results.

This level is similar to the sixth layer, but additionally includes sample output from the prompt. This helps the AI generate more accurate and relevant responses, often referred to as "one-off" and "few-time" prompts.

Step 1: Copy and paste text blocks, data, or other content from external sources here

Step 2: Enter the task requested by the user Step 3: Enter the guide to using context to perform the task Step 4: Sample output (1 or 2 examples)

Tier 10: Advanced Thinking Tree Prompts that simulate human-like problem-solving techniques.

These prompts utilize a "thinking tree" (ToT) approach to mimic similar problem-solving strategies in humans. This setup improves the problem-solving capabilities of language learning models (LLMs), allowing them to interact back and forth with artificial intelligence. This approach involves multiple components.

1. "Prompter Agent" drafts initial questions or questions that LLM needs to solve.

2. "LLM" is dedicated to solving or answering questions and generating corresponding output.

3. The "Inspection Module" evaluates the output of the LLM to determine its correctness or suitability in solving the problem.

4. "Memory Modules" record ongoing conversations and progress in problem solving.

5. The "Tot Controller" uses feedback from the inspector and enclosure to decide on the next move. This may include going back to a previous step or changing the method.

6. The cycle continues until the problem at hand is resolved. The ToT method can be used to manage various situations, from things like Sudoku

7. High-complexity tasks such as puzzles to tasks that require creative problem-solving. Advanced "thinking tree" prompts are needed

8. Carefully constructed and strategically used according to the specifics of the problem, the capabilities of the LLM, and the complexity of the ToT system.

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