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Popular Niches and Micro Niches

A niche is a market or segment of people or a specific set of products. A micro-niche is a subcategory inside of a broader niche. The difference between a “macro-niche” and a “micro-niche is the difference between a general interest and a laser focus.

chatgpt prompt for solar energy

Explicitly state the task to be performed to get the required output
Role of the language model for performing the task
Context in which the task must be done
Guidelines on how to perform the task
The desired output format of the ideal response

Delimiters in the prompt engineering community include:

Triple quotes: """

Hashtags: ####

Triple back-ticks: ```

Triple dashes: ---

Angle brackets: < >

XML tags: <tag></tag>

Reasons why role-based prompts are effective:

Narrowing down the domain: LLMs understand the statistical nature of languages but lack inherent knowledge or understanding of specific domains or concepts. By specifying an area of expertise, the prompt restricts the model’s response generation to that particular domain, focussing its attention on just the relevant information and filtering out irrelevant or off-topic responses.
Guiding response generation: Role-based prompts can direct the model’s thinking and reasoning towards the desired area of expertise. For example, the prompt may imply that the response should involve mathematical concepts, formulas or problem-solving strategies.
Setting user expectations: Role-based prompts help establish clear expectations for users regarding the type of response they can anticipate. In the above example, the model will provide answers from the perspective of a mathematics expert, possibly with advanced knowledge and reasoning abilities.
Leveraging general knowledge: While language models lack specific expertise, they have access to vast amounts of general knowledge. Role-based prompts can trigger the model to recall and utilise relevant information from its training data, which may include mathematical principles, theorems or problem-solving techniques.

Use delimiters in the following manner:

Separating input and output instructions
Demarcating the system message from the rest of the instructions
Separating conditional statements

Demarcating different sections within the prompt, allowing for:

More precise instructions
Greater flexibility and control
Enhancing the model’s understanding of the prompt’s context and desired output

1. Ask me what the topic of the prompt is.
2. After I submit my prompt, generate 3 sections: (a) your revised prompt; (b) suggestions on how to improve the prompt; (c) clarifying questions.
3. Continue this iterative process, by asking me for additional input, until I am satisfied with the prompt.
4. After three iterations, ask me whether you should execute the prompt or whether we should continue iterating. Do this for every iteration after the third iteration.

Click on Gemini, ChatGPT, or Claud to explore further by framing suitable prompts or read topics from Google and Translate the content to read and listen in various languages like Hindi, Urdu, Gujarati, Tamil, Bengali, and more.

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