Included prompt template and lazy editing of chat gpt putting my notes together paper for rough draft to mark it first. Using Blocks of perspectives and skills.

README.md

@@ -13,4 +13,48 @@ My Goal as an AI Researcher is to make smarter models, and sometimes alignment a
 
 Further testing on reasoning domains is required as it appears the model makes its best attempt at any task provided without any restraint.
 
-Best, TroyDoesAI
+Best, TroyDoesAI
+
+### Abstract
+This paper presents a method for structuring training prompts in language models to enhance response relevance and contextual accuracy using the keyword `perspective`. This approach leverages `perspective` to guide the model in generating responses that reflect different viewpoints or interpretations of input queries.
+
+### Introduction
+Effective language models require precise mechanisms for generating contextually appropriate responses. The term `perspective` offers a multifaceted approach to frame responses, addressing both conceptual viewpoints and visual contexts. This research explores the use of `perspective` in prompt templates to direct model outputs according to specified contexts.
+
+### Methodology
+The proposed prompt template is:
+```
+"perspective,input,output": "<s> [INST] [%perspective%] %input% [/INST] [/perspective]: %output%</s>"
+```
+- **`[INST]` and `[/INST]`**: Wrap instructions for context.
+- **`[%perspective%]`**: Placeholder for specifying the viewpoint or context.
+- **`%input%`**: Represents the user's query.
+- **`[/perspective]: %output%`**: Delineates the response section according to the given perspective.
+
+### Definitions and Rationale
+1. **Perspective** can refer to:
+   - **Viewpoint**: The angle or opinion from which something is considered.
+   - **Visible Scene**: The spatial or visual representation of a scene.
+   - **Spatial Representation**: In art, how objects are depicted to convey depth and distance.
+
+   By incorporating `perspective`, the model can frame responses to reflect various viewpoints, enhancing response relevance.
+
+### Application
+Incorporating `perspective` into training prompts ensures that responses are:
+- **Contextually Relevant**: Aligning with the specified viewpoint.
+- **Nuanced**: Addressing different angles and interpretations.
+- **Consistent**: Providing uniform guidance for generating responses.
+
+For example, querying "How does climate change affect coastal cities?" with a `perspective` keyword allows the model to generate responses from environmental, economic, or social viewpoints, thus enriching the answer's depth.
+
+### Results and Benefits
+Using `perspective` as a keyword in prompt templates leads to:
+- Improved relevance and contextual accuracy of responses.
+- Enhanced ability to address complex queries from multiple angles.
+- Consistent response structure facilitating model training and application.
+
+### Conclusion
+Employing `perspective` in language model prompt templates effectively directs responses according to specified contexts, improving both relevance and clarity. This method provides a structured approach for generating nuanced and contextually accurate outputs.
+
+### Keywords
+Language model, perspective, prompt template, contextual accuracy, response relevance.