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Prompt Engineering - Chain-of-Thought, Few-Shot, RAG Prompting Patterns

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Execution Trace
Step 1/4
Few-Shot Examples

Model receives two examples: 'Example 1' and 'Example 2' to understand task context.

🎯 Demonstrates how few-shot learning provides context to the model.
examples = ['Example 1', 'Example 2']
prompt = f'Examples: {examples}'
Chain-of-Thought

Model processes step-by-step logic to solve a problem.

🎯 Highlights the importance of logical reasoning in complex tasks.
logic_steps = 'Step-by-step Logic'
prompt += f' Logic: {logic_steps}'
RAG (Retrieval-Augmented Generation)

Model retrieves external knowledge to enhance response accuracy.

🎯 Shows how external data can improve model performance.
external_knowledge = 'External Knowledge'
prompt += f' Knowledge: {external_knowledge}'
Output Response

Model generates a response based on the structured prompt.

🎯 Demonstrates the final output generation from the engineered prompt.
response = model.generate(prompt)
print(response)
📊
Diagram
Diagram
┌──────────────────────────────────────────────────────────────┐
│                      Prompt Engineering                      │
├──────────────────────────────────────────────────────────────┤
│  Input Prompt                                                │
│  ┌──────────────────────────┐                                 │
│  │  Few-Shot Examples       │                                 │
│  │  ┌───────────────┐       │                                 │
│  │  │ Example 1     │       │                                 │
│  │  └───────────────┘       │                                 │
│  │  ┌───────────────┐       │                                 │
│  │  │ Example 2     │       │                                 │
│  │  └───────────────┘       │                                 │
│  └──────────────────────────┘                                 │
│  ┌──────────────────────────┐                                 │
│  │  Chain-of-Thought        │                                 │
│  │  ┌────────────────────┐  │                                 │
│  │  │ Step-by-step Logic │  │                                 │
│  │  └────────────────────┘  │                                 │
│  └──────────────────────────┘                                 │
│  ┌──────────────────────────┐                                 │
│  │  RAG (Retrieval-Augmented)│                                │
│  │  ┌────────────────────┐  │                                 │
│  │  │ External Knowledge │  │                                 │
│  │  └────────────────────┘  │                                 │
│  └──────────────────────────┘                                 │
│  Output Response                                              │
└──────────────────────────────────────────────────────────────┘
Labels
  • Few-Shot Examples: Demonstrates task with examples
  • Chain-of-Thought: Guides logical reasoning
  • RAG: Integrates external knowledge
  • Input Prompt: Structured input for the model
  • Output Response: Model's generated answer
🔑
Key Moments
▲
Q: Why is the Few-Shot example important in prompt engineering?
A: It provides context and examples to the model, helping it understand the task better.
Q: How does Chain-of-Thought improve model reasoning?
A: It guides the model through logical steps, enhancing its ability to solve complex problems.
Q: What role does RAG play in prompt engineering?
A: RAG integrates external knowledge, improving the model's accuracy and relevance of responses.
Trace Summary

This diagram explains the mechanism of prompt engineering using Few-Shot, Chain-of-Thought, and RAG patterns. Few-Shot examples provide context, Chain-of-Thought guides logical reasoning, and RAG incorporates external knowledge. Together, these elements structure the input prompt, enabling the model to generate accurate and contextually relevant responses. Understanding these components is crucial for designing effective prompts in production ML systems.

Primary Focus

✓ Understanding the mechanism of prompt engineering patterns in ML

Practice

(1/5)
1. Given the following MLflow code snippet, what will be the output?
import mlflow
mlflow.start_run()
mlflow.log_param('alpha', 0.5)
mlflow.log_metric('accuracy', 0.85)
mlflow.end_run()
What happens when you run this code?
easy
A. It logs the parameter and metric successfully without any errors.
B. It logs the parameter and metric but throws an error due to missing experiment ID.
C. It logs only the parameter, not the metric.
D. It throws an error because the run is not active.

Solution

  1. Step 1: Understand MLflow run lifecycle

    MLflow allows logging parameters and metrics within an active run.

  2. Step 2: Analyze the code

    The code starts a run, logs a parameter and a metric, and ends the run correctly.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    Active run = Log success [OK]
Hint: Active run = Log success [OK]
Common Mistakes:
2. 
import numpy as np
from sklearn.preprocessing import StandardScaler

X = np.array([[1, 2], [3, 4], [5, 6]])
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
print(X_scaled.mean(axis=0))
What does this code output?
easy
A. [1. 1.]
B. [0.5 0.5]
C. [0. 0.]
D. [0. 1.]

Solution

  1. Step 1: Understand standardization.

    StandardScaler standardizes features by removing the mean and scaling to unit variance.

  2. Step 2: Calculate the mean of standardized data.

    After standardization, the mean of each feature should be 0.

  3. Final Answer:

    Option C -> Option C

  4. Quick Check:

    StandardScaler centers data to mean 0. [OK]
Hint: StandardScaler centers data to mean 0. [OK]
Common Mistakes:
3. A model trained with weak supervision is underperforming. What is the most likely cause?
medium
A. The heuristics used are too simplistic.
B. The model architecture is too complex.
C. The dataset size is too small.
D. The learning rate is too high.

Solution

  1. Step 1: Identify the weak supervision challenge.

    Weak supervision relies on heuristics, which can be a source of error if too simplistic.

  2. Step 2: Eliminate other causes.

    Model complexity and dataset size are less likely to be the primary issue in weak supervision. Learning rate affects training stability, not heuristic quality.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    Weak supervision = heuristic quality [OK]
Hint: Weak supervision = heuristic quality [OK]
Common Mistakes:
4. In a scenario where computational resources are limited, which approach is more suitable for hyperparameter tuning and why?
medium
A. Use a train-validation split and skip the test set.
B. Use a single train-test split to save resources.
C. Use cross-validation to ensure robust hyperparameter tuning.
D. Use a larger test set to ensure model generalization.

Solution

  1. Step 1: Evaluate resource constraints

    Cross-validation is computationally expensive but provides robust tuning.

  2. Step 2: Eliminate less suitable options

    Use a single train-test split to save resources. risks overfitting. Use a train-validation split and skip the test set. skips the test set, risking unseen data performance. Use a larger test set to ensure model generalization. misallocates resources to testing rather than tuning.

  3. Final Answer:

    Option C -> Option C

  4. Quick Check:

    Cross-validation ensures robust tuning despite cost. [OK]
Hint: Cross-validation ensures robust tuning despite cost. [OK]
Common Mistakes:
5. You need to reduce the dimensionality of a dataset with 10,000 features for a real-time application. Which technique is more suitable and why?
medium
A. t-SNE, because it captures non-linear relationships effectively.
B. PCA, because it is computationally efficient for large datasets.
C. UMAP, because it is faster than t-SNE and captures global structure.
D. LDA, because it reduces dimensions based on class labels.

Solution

  1. Step 1: Consider computational efficiency

    Real-time applications require fast computations.

  2. Step 2: Evaluate each option

    t-SNE is computationally expensive, UMAP is faster but still slower than PCA, and LDA is not suitable for unsupervised tasks.

  3. Final Answer:

    Option B -> Option B

  4. Quick Check:

    PCA for speed in large datasets [OK]
Hint: PCA for speed in large datasets [OK]
Common Mistakes:
Recommended

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10 questions~5 mineasy hard