Update docs/llm/Effective-LLM-Prompting.md

docs/llm/Effective-LLM-Prompting.md

@@ -62,6 +62,37 @@ This guide is crafted to empower developers and enthusiasts in creating effectiv
 - ❌ "Explain machine learning."
 - ✅ "Write a comprehensive explanation of machine learning for a layman, including practical examples, without using jargon."
 
+## 💡 Practical Application: Iterating on Prompts Based on LLM Responses
+
+This section offers practical strategies for refining prompts based on the responses from Language Learning Models (LLMs), which is crucial for achieving the most accurate and relevant outputs.
+
+### 🔄 Iterative Refinement Process
+- **Initial Evaluation**: Critically assess if the LLM's response aligns with the prompt's intent.
+- **Identify Discrepancies**: Locate areas where the response differs from the expected outcome.
+- **Adjust for Clarity**: Refine the prompt to clarify the expected response.
+- **Feedback Loop**: Use the LLM's output to iteratively adjust the prompt for better accuracy.
+
+### 📋 Common Issues & Solutions
+- **Overly Broad Responses**: Specify the scope and depth required in the prompt.
+- **Under-Developed Answers**: Ask for explanations or examples to enrich the response.
+- **Misalignment with Intent**: Clearly state the purpose of the information being requested.
+- **Incorrect Assumptions**: Add information to the prompt to correct the LLM's assumptions.
+
+### 🛠 Tools for Refinement
+- **Contrastive Examples**: Use 'do's and 'don'ts' to clarify task boundaries.
+- **Sample Outputs**: Provide examples of desired outputs.
+- **Contextual Hints**: Embed hints in the prompt to guide the LLM.
+
+### 🎯 Precision in Prompting
+- **Granular Instructions**: Break down tasks into smaller steps.
+- **Explicit Constraints**: Define clear boundaries and limits for the task.
+
+### 🔧 Adjusting Prompt Parameters
+- **Parameter Tuning**: Experiment with verbosity, style, or tone settings.
+- **Prompt Conditioning**: Prime the LLM with a series of related prompts before the main question.
+
+Implementing these strategies can significantly improve the effectiveness of your prompts, leading to more accurate and relevant LLM outputs.
+
 ## 🔚 Conclusion
 This guide is designed to help refine your prompt crafting skills, enabling more effective and efficient use of LLMs for a range of applications.
 
@@ -116,3 +147,14 @@ This taxonomy provides a hierarchical framework for categorizing educational obj
 - **Creating**: Innovate and formulate new concepts or products.
   - Initiate and develop original creations or ideas that enhance or extend existing paradigms.
 
+# Latent Content
+
+This term refers to the reservoir of knowledge, facts, concepts, and information that is integrated within a model and requires activation through effective prompting.
+
+- **Training Data**: Source of latent content derived exclusively from the data used during the model's training process.
+- **World Knowledge**: Broad facts and insights pertaining to global understanding.
+- **Scientific Information**: Detailed data encompassing scientific principles and theories.
+- **Cultural Knowledge**: Insights relating to various cultures and societal norms.
+- **Historical Knowledge**: Information on historical events and notable individuals.
+- **Languages**: The structural elements of language, including grammar, vocabulary, and syntax.
+