prompt engineering techniques

# Prompt Engineering Techniques

## Introduction & Motivation

Prompt engineering: craft inputs for optimal LLM output. Critical for zero-shot and few-shot learning. Applications: improving model performance without retraining.

Motivation: Guide language models effectively through prompting.

Applications: Task adaptation, zero-shot learning, few-shot examples.

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## Core Concepts & Theory

### Prompt Design

Structure input for clarity.

### In-Context Examples

Demonstrate desired behavior.

### Instruction Following

Clear task specification.

### Output Formatting

Specify expected format.

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## Mathematical Formulation

Prompt Structure:
$$P = [I, E_1, ..., E_k, T]$$

Where I = instruction, E = examples, T = target.

Effectiveness:
$$ ext{Performance} = f( ext{prompt quality})$$

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## Advanced Theory & Extensions

### Chain-of-Thought

Multi-step reasoning prompts.

### Few-Shot Learning

Demonstration-based adaptation.

### Role-Based Prompting

Assign personas to model.

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## Computational Considerations

Prompt length: Variable.

Inference cost: O(prompt length).

Memory: Stored in context.

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## Practical Implementation Strategies

### Clear Instructions

Explicit task specification.

### Example Selection

Choose representative examples.

### Format Specification

Define output format.

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## Benchmark Datasets & Evaluation

GLUE: Text understanding.

SuperGLUE: Challenging tasks.

Custom metrics: Task-specific.

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## Key Challenges & Limitations

### Sensitivity

Results vary with phrasing.

### Scalability

Manual design needed per task.

### Generalization

Limited transfer to new domains.

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## Hyperparameter Tuning

Number of examples: 0-16.

Example selection: Random or semantic.

Prompt format: Task-specific.

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## Real-World Applications & Case Studies

Code Generation: Prompt for programming tasks.

Content Creation: Generate diverse outputs.

Chatbots: Guide conversational behavior.

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## Integration with Other Methods

Prompt engineering + fine-tuning for optimal results.

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## Summary & Key Takeaways

Prompt engineering enables effective LLM use.

Principles:
1. Clarity: Explicit instructions.
2. Examples: Demonstrate behavior.
3. Structure: Consistent format.
4. Specificity: Task-targeted.
5. Iteration: Refine prompts.

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## Appendix: Practical Labs

### Lab 1: Template-Based Prompting

def create_prompt(task, input_text, examples=None):
 """Create structured prompt"""
 prompt = f"Task: {task}

"
 
 if examples:
 prompt += "Examples:
"
 for ex in examples:
 prompt += f"Input: {ex['input']}
Output: {ex['output']}

"
 
 prompt += f"Input: {input_text}
Output:"
 return prompt

task = "Sentiment classification"
examples = [
 {"input": "Great movie!", "output": "Positive"},
 {"input": "Terrible experience", "output": "Negative"}
]
prompt = create_prompt(task, "Amazing food", examples)
assert "Sentiment classification" in prompt
print("✓ Template-based prompting working")

### Lab 2: Example Selection

import numpy as np

def select_similar_examples(query, corpus, k=3):
 """Select k examples most similar to query"""
 # Simplified similarity
 similarities = []
 for doc in corpus:
 sim = len(set(query.split()) & set(doc.split())) / max(len(set(query.split())), 1)
 similarities.append(sim)
 
 indices = np.argsort(similarities)[-k:][::-1]
 return [corpus[i] for i in indices]

corpus = ["Good movie", "Bad film", "Excellent performance", "Poor quality"]
selected = select_similar_examples("Nice film", corpus, k=2)
assert len(selected) <= 3
print(f"✓ Example selection: {selected}")

### Lab 3: Prompt Optimization

def evaluate_prompts(prompts, scorer_func):
 """Evaluate multiple prompts"""
 scores = []
 for prompt in prompts:
 score = scorer_func(prompt)
 scores.append(score)
 
 best_idx = np.argmax(scores)
 return prompts[best_idx], scores[best_idx]

def dummy_scorer(prompt):
 return len(prompt) % 10 # Simplified scoring

prompts = ["Do this", "Please do this task carefully", "Execute task X"]
best_prompt, score = evaluate_prompts(prompts, dummy_scorer)
assert best_prompt in prompts
print(f"✓ Best prompt found with score {score}")

### Lab 4: Format Specification

def specify_output_format(task, format_type="json"):
 """Add output format specification"""
 format_specs = {
 "json": "Return result as JSON object",
 "list": "Return result as comma-separated list",
 "text": "Return as natural language"
 }
 
 prompt = f"Task: {task}
Output format: {format_specs.get(format_type, format_specs['text'])}"
 return prompt

prompt = specify_output_format("Extract entities", format_type="json")
assert "JSON" in prompt
print("✓ Format specification working")

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