Merge pull request #41 from Srishy/main

added README file in prompts folder

Author: Sathvika-891

maslibpy/prompts/README.md

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+# Prompt type-and Pattern Combinations
+
+---
+
+## 1. Critique- Meta-Llama-3.1-8B-Instruct-Turbo & Generator- Mistral-7B-Instruct-v0.1
+
+**Pattern : React-React(default React)**
+
+**User query:**  Explain the concept of backpropagation in neural networks
+
+**Final Answer:**
+
+Backpropagation is a technique used in neural networks to adjust the weights of the connections between neurons based on the error between the predicted output and the actual output. This is done by computing the gradient of the loss function with respect to each weight, and then updating the weights using an optimization algorithm such as stochastic gradient descent. The process involves two main steps: forward pass, where the input is propagated through the network to produce the output, and backward pass, where the error is propagated backwards through the network to compute the gradients of the loss function with respect to each weight. 
+
+---
+
+## 2. Critique- Meta-Llama-3.1-8B-Instruct-Turbo & Generator- Meta-Llama-3.1-8B-Instruct-Turbo
+
+**Pattern : React-Reflection**
+
+**User query:** Explain the concept of backpropagation in neural networks
+ 
+**Final Answer:**
+
+Backpropagation is a technique used in neural networks to adjust the weights of the connections between neurons based on the error between the predicted output and the actual output. This is done by computing the gradient of the loss function with respect to each weight, and then updating the weights using an optimization algorithm such as stochastic gradient descent. The process involves two main steps: forward pass, where the input is propagated through the network to produce the output, and backward pass, where the error is propagated backwards through the network to compute the gradients of the loss function with respect to each weight. 
+   
+---
+
+## 3. Critique- Meta-Llama-3.1-70B-Instruct-turbo & Generator- Mistral-7B-Instruct-v0.1
+
+**Pattern: React-Reflexion**
+
+**User query:** Explain the concept of backpropagation in neural networks
+
+**Final Answer:**
+
+Backpropagation is a technique used in neural networks to adjust the weights of the connections between neurons in order to minimize the error between the predicted output and the actual output. It works by propagating the error backwards through the network, adjusting the weights at each layer based on the gradient of the error with respect to the weights. This process is typically performed using an optimization algorithm such as stochastic gradient descent (SGD) or Adam, which minimizes the loss function (e.g., mean squared error or cross-entropy). Regularization techniques, such as L1 or L2 regularization, are also used to prevent overfitting. The goal of backpropagation is to enable the neural network to learn from its mistakes and improve its performance over time.
+
+            Return exactly 'True' if all criteria are met, or exactly 'False' if any criterion fails.
+            Do not include any reasoning, explanations, or additional characters - your entire output must be either the word 'True' or the word 'False'
+
+---
+
+## 4. Critique- Meta-Llama-3.1-405B-Instruct-turbo & Generator- Mistral-7B-Instruct-v0.1
+
+**Pattern : React-Rewoo**
+
+**User query:** Explain the concept of backpropagation in neural networks
+
+**Final Answer:**
+
+Sure, I'd be happy to explain the concept of backpropagation in neural networks.
+
+Backpropagation is a technique used in neural networks to train the network to make better predictions. It works by adjusting the weights of the connections between the neurons in the network based on the error between the predicted output and the actual output.
+
+The process of backpropagation involves two main steps:
+
+1. Forward Propagation: The input data is fed into the neural network, and the output is generated.
+2. Backward Propagation: The error between the predicted output and the actual output is calculated, and the weights of the connections between the neurons are adjusted to minimize the error.
+
+The adjustment of the weights is done using a gradient descent algorithm, which calculates the gradient of the error with respect to the weights and updates the weights in the opposite direction of the gradient. This process is repeated for multiple iterations until the error is minimized to an acceptable level.
+
+Backpropagation is a powerful technique that allows neural networks to learn from their mistakes and improve their performance over time. It is widely used in a variety of applications, including image recognition, speech recognition, and natural language processing.
+
+---
+
+## 5. Critique- Mistral-7B-Instruct-v0.1 & Generator- Meta-Llama-3.1-405B-Instruct-turbo
+
+**Pattern: Cot-Cot**
+
+**User query:** Explain the concept of backpropagation in neural networks
+
+**Final Answer:**
+
+Backpropagation is a supervised learning algorithm used in neural networks to minimize the error between the predicted output and the actual output. It involves a forward pass, error calculation, backward pass, and weight update, which are repeated until the network converges to a minimum error. The key components of backpropagation include activation functions, loss functions, gradients, and learning rate.
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