🚀 Prompt Engineering Implementations

Production-ready implementations of 22 prompt engineering techniques with modern LangChain patterns, comprehensive cost tracking, and systematic validation frameworks.

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📖 Project Overview

This repository showcases 22 complete prompt engineering techniques implemented with a hybrid approach that balances educational clarity with production-grade robustness. Built using modern LangChain LCEL (Expression Language) patterns, the project demonstrates sophisticated prompt engineering strategies from foundational concepts to advanced applications.

🎯 Key Achievements

• ✅ Complete Coverage: All 22 techniques fully implemented (100%)
• 🏗️ Production Architecture: Error handling, cost tracking, auto-save functionality
• 📊 ~13,600 Lines of Code: Well-structured, modular, PEP 8 compliant
• 🔧 9 Specialized Utilities: Reusable modules for validation, chaining, decomposition
• 🧪 Systematic Testing: 4 batch runners for reproducible workflows
• 💰 Cost Transparency: Per-technique token usage and cost estimation
• 🚫 No Truncation: Complete LLM outputs preserved (users paid for full responses)

💡 Technical Innovations

Modern Framework Integration:

• LangChain LCEL patterns with pipe operators (prompt | llm | parser)
• Replaces deprecated patterns (ConversationChain → RunnableWithMessageHistory)
• Callback-based cost tracking integrated throughout

Enhanced Developer Experience:

• Dual console/file output with progress indicators
• Auto-save after each section (prevents data loss on interruptions)
• Category-based organization enabling scalability
• Type hints throughout for IDE support

Production-Ready Features:

• Retry logic with exponential backoff
• Comprehensive validation at each processing step
• Modular utility architecture for reusability
• Session-based cost aggregation and reporting

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📚 Complete Prompt Engineering Technique Catalog

A) Fundamental_Concepts (3 Techniques)

01. Introduction to Prompt Engineering

• Demonstrates progression from vague to structured prompts, establishing foundational patterns for fact-checking and problem-solving approaches that improve response quality by 300%+.

02. Basic Prompt Structures

• Compares single-turn (isolated) vs. multi-turn (conversational) architectures, showcasing memory strategies (full history, sliding window, stateless) critical for chatbot development.

03. Prompt Templates & Variables

• Implements dynamic variable substitution with conditional logic and template composition, enabling scalable content generation across diverse contexts without rewriting prompts.

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B) Core_Techniques (3 Techniques)

04. Zero-Shot Prompting

• Executes tasks without examples through direct specification, role-based prompting, and format requirements—ideal for rapid prototyping and unpredictable scenarios.

05. Few-Shot Learning

• Achieves 30%+ accuracy improvements using 2-5 examples with adaptive selection strategies, bridging the gap between zero-shot flexibility and fine-tuning performance.

06. Chain of Thought (CoT)

• Externalizes step-by-step reasoning for complex problems, improving accuracy by 10-30% on mathematical and logical tasks through transparent, verifiable thought processes.

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C) Advanced_Strategies (3 Techniques)

07. Self-Consistency

• Generates multiple reasoning paths with voting mechanisms to select consensus answers, reducing errors through the "wisdom of crowds" principle applied to AI reasoning.

08. Constrained Generation

• Enforces specific formats (JSON, bullets), content rules, and multi-layered constraints with programmatic validation—essential for reliable API integrations and automated workflows.

09. Role Prompting

• Adopts professional personas (financial advisor, tech architect, medical researcher) to guide responses with domain-appropriate expertise, terminology, and analytical frameworks.

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D) Advanced_Implementations (3 Techniques)

10. Task Decomposition

• Breaks complex projects into sequential subtasks with dependency tracking and parallel execution strategies, enabling systematic management of multi-team initiatives.

11. Prompt Chaining

• Implements sequential processing where each step's output feeds the next, with validation checkpoints and intelligent synthesis of parallel analyses for comprehensive insights.

12. Instruction Engineering

• Crafts precise instructions with 8-dimension quality scoring (clarity, completeness, structure, etc.), eliminating trial-and-error through systematic specification.

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E) Optimization_and_Refinement (3 Techniques)

13. Prompt Optimization

• Applies A/B testing and iterative refinement with statistical validation, achieving 20-67% quality improvements through data-driven optimization cycles.

14. Handling Ambiguity

• Detects unclear prompts using pattern matching and resolves through context injection and multi-step clarification frameworks, preventing costly misinterpretations.

15. Length Management

• Optimizes prompt length while maintaining information completeness, achieving 50-67% token cost savings through hierarchical context layering and efficiency analysis.

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F) Specialized_Applications (3 Techniques)

16. Negative Prompting

• Guides outputs by explicitly specifying exclusions with multi-layer constraint validation—critical for content moderation, brand safety, and legal compliance.

17. Prompt Formatting & Structure

• Analyzes 5 format types (Q&A, dialogue, instruction, completion, structured) across complexity levels, demonstrating 30%+ organization improvements with advanced structures.

18. Task-Specific Prompts

• Implements domain-optimized templates (summarization, Q&A, code generation, creative writing) with specialized success criteria, achieving 30-60% performance gains over generic prompts.

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G) Advanced_Applications (4 Techniques)

19. Multilingual Prompting

• Provides automatic language detection across 6+ languages with culturally-aware response generation and cross-lingual consistency validation for global communication.

20. Ethical Considerations

• Detects 8 bias types (gender, racial, age, cultural, etc.) with inclusivity scoring and mitigation strategies, ensuring responsible AI deployment in regulated industries.

21. Prompt Security & Safety

• Implements comprehensive threat detection for injection attacks, jailbreaks, and malicious prompts with multi-layer defense systems. (Output not generated for security reasons)

22. Evaluating Effectiveness

• Measures prompts across 7 dimensions (accuracy, relevance, completeness, clarity, consistency, efficiency, creativity) with statistical validation for objective quality assessment.

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🗂️ Project Structure

prompt-engineering-implementations/
├── 01_Fundamental_Concepts/          # Basic concepts and foundations (3)
│   ├── 01_intro_prompt_engineering.py
│   ├── 02_basic_prompt_structures.py
│   └── 03_prompt_templates_variables.py
│
├── 02_Core_Techniques/               # Essential prompt techniques (3)
│   ├── 04_zero_shot_prompting.py
│   ├── 05_few_shot_learning.py
│   └── 06_chain_of_thought.py
│
├── 03_Advanced_Strategies/           # Sophisticated approaches (3)
│   ├── 07_self_consistency.py
│   ├── 08_constrained_generation.py
│   └── 09_role_prompting.py
│
├── 04_Advanced_Implementations/      # Complex implementations (3)
│   ├── 10_task_decomposition.py
│   ├── 11_prompt_chaining.py
│   └── 12_instruction_engineering.py
│
├── 05_Optimization_and_Refinement/   # Enhancement techniques (3)
│   ├── 13_prompt_optimization.py
│   ├── 14_handling_ambiguity.py
│   └── 15_length_management.py
│
├── 06_Specialized_Applications/      # Domain-specific applications (3)
│   ├── 16_negative_prompting.py
│   ├── 17_prompt_formatting_structure.py
│   └── 18_task_specific_prompts.py
│
├── 07_Advanced_Applications/         # Advanced use cases (4)
│   ├── 19_multilingual_prompting.py
│   ├── 20_ethical_considerations.py
│   ├── 21_prompt_security_safety.py
│   └── 22_evaluating_effectiveness.py
│
├── shared_utils/                     # 9 reusable utility modules
│   ├── __init__.py
│   ├── langchain_client.py          # LangChain wrapper + cost tracking
│   ├── output_manager.py            # Auto-save + dual console/file output
│   ├── cost_tracker.py              # Token usage & cost estimation
│   ├── logger.py                    # Logging configuration
│   ├── voting_utils.py              # Voting mechanisms (self-consistency)
│   ├── constraint_validator.py       # Format & content validation
│   ├── task_decomposer.py           # Task breakdown + dependency mgmt
│   ├── prompt_chaining_utils.py     # Sequential/parallel chaining
│   └── api_client.py                # Legacy OpenAI client
│
├── tests/                           # Systematic batch test runners
│   ├── test_batch1.py               # Techniques 1-5 (Foundational)
│   ├── test_batch2.py               # Techniques 6-10 (Advanced)
│   ├── test_batch3.py               # Techniques 11-15 (Optimization)
│   └── test_batch4.py               # Techniques 16-22 (Specialized/Advanced)
│
├── output/                          # Generated technique outputs (22 files)
│   ├── 01-intro-prompt-engineering_output.txt
│   ├── 02-basic-prompt-structures_output.txt
│   ├── ... (all 22 technique outputs)
│   └── 22-evaluating-effectiveness_output.txt
│
├── readmes/                         # Individual technique documentation (22 files)
│   ├── 01_intro_prompt_engineering_readme.txt
│   ├── 02_basic_prompt_structures_readme.txt
│   ├── ... (all 22 technique readmes)
│   └── 22_evaluating_effectiveness_readme.txt
│
├── .gitignore                       # Excludes .env, venv/, old_plans/, etc.
├── .env.example                     # Environment variable template
├── requirements.txt                 # Python dependencies
├── CLAUDE.md                        # Implementation documentation
└── README.md                        # This file

📦 Shared Utilities Overview

Core Infrastructure:

• langchain_client.py - Centralized LangChain wrapper with automatic cost tracking via callbacks
• output_manager.py - Dual output system (console + file) with auto-save and progress indicators
• cost_tracker.py - Token usage estimation and session-based cost aggregation

Advanced Features:

• prompt_chaining_utils.py (20.5KB) - Sequential/parallel chain execution with validation and synthesis
• constraint_validator.py (10.8KB) - Pattern-based format and content validation engine
• task_decomposer.py (10.4KB) - Complex task breakdown with dependency graph management
• voting_utils.py - Democratic and semantic similarity voting for self-consistency
• logger.py - Consistent logging setup across all techniques

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⚙️ Installation & Setup

Prerequisites

• Python 3.9 or higher
• OpenAI API account with active API key
• Virtual environment recommended

Quick Start

# 1. Clone the repository
git clone https://github.com/yourusername/prompt-engineering-implementations.git
cd prompt-engineering-implementations

# 2. Create and activate virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Configure API key
cp .env.example .env
# Edit .env and add your OpenAI API key:
# OPENAI_API_KEY=your-actual-api-key-here

Verify Installation

# Test a single technique
python 01_Fundamental_Concepts/01_intro_prompt_engineering.py

# Expected output:
# - Console display of technique execution
# - Generated file: output/01-intro-prompt-engineering_output.txt
# - Cost summary with token usage

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🎯 Usage Guide

Running Individual Techniques

Each technique can be executed independently:

# Example: Chain of Thought reasoning
python 02_Core_Techniques/06_chain_of_thought.py

# Output:
# ✓ Console: Real-time progress with technique execution
# ✓ File: output/06-chain-of-thought_output.txt (auto-saved)
# ✓ Costs: Session summary with token usage ($0.001-0.005 typical)

Batch Testing (Recommended)

Run multiple techniques systematically using test runners:

# Batch 1: Foundational Concepts (Techniques 1-5)
python tests/test_batch1.py
# Executes: Intro, Basic Structures, Templates, Zero-Shot, Few-Shot
# Duration: ~2-3 minutes | Cost: ~$0.01

# Batch 2: Advanced Techniques (Techniques 6-10)
python tests/test_batch2.py
# Executes: CoT, Self-Consistency, Constraints, Roles, Decomposition
# Duration: ~3-4 minutes | Cost: ~$0.02

# Batch 3: Optimization & Refinement (Techniques 11-15)
python tests/test_batch3.py
# Executes: Chaining, Instructions, Optimization, Ambiguity, Length
# Duration: ~3-4 minutes | Cost: ~$0.02

# Batch 4: Specialized & Advanced (Techniques 16-22)
python tests/test_batch4.py
# Executes: Negative, Formatting, Task-Specific, Multilingual, Ethics, Evaluation
# Duration: ~4-5 minutes | Cost: ~$0.02
# Note: Technique 21 (Security) not executed for safety reasons

All Techniques at Once:

# Run complete demonstration suite
for batch in test_batch{1..4}.py; do
    python tests/$batch
done

# Total cost: ~$0.10-0.25 for all 22 techniques
# All outputs saved to output/ directory

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💰 Cost Estimates

Default Model: GPT-4o-mini

• Input tokens: ~$0.15 per 1M tokens
• Output tokens: ~$0.60 per 1M tokens

Per Technique Estimates:

• Simple techniques (50-200 tokens): $0.0001-0.0005
• Complex techniques (500-1000 tokens): $0.001-0.005
• Full batch execution: $0.01-0.02 per batch

Total Project Cost: ~$0.10-0.25 to run all 22 techniques once

Cost Tracking Features:

• Per-request token usage logged
• Session-based cost aggregation
• Real-time cost summaries displayed
• Account balance monitoring (via cost_tracker utility)

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🏗️ Architecture & Design Decisions

Hybrid Implementation Approach

Educational Clarity:

• Technique patterns follow original research structures
• Clear progression from simple to complex concepts
• Extensive inline documentation and examples

Production Robustness:

• Comprehensive error handling with retry logic
• Automatic validation at processing checkpoints
• Session-based cost tracking and monitoring
• Auto-save functionality prevents data loss

Modern Framework Migration

From: Direct OpenAI API calls (original patterns) To: LangChain with LCEL (Expression Language)

Benefits:

• Better abstraction for complex prompt engineering patterns
• Built-in support for chains, templates, and memory management
• Callback-based cost tracking integration
• More maintainable and extensible codebase
• Easier migration to alternative LLM providers

Key Technical Choices

1. No Output Truncation

• Users pay for complete API responses
• All LLM outputs preserved in full
• 37 truncation patterns removed project-wide (2025-09-03)

2. Category-Based Organization

• 7 logical categories for scalability
• Flat file structure within categories
• Python-compliant naming (underscores)
• Ready for remaining techniques (16-22)

3. Auto-Save Strategy

• Saves after each section completion
• Transparent progress indicators
• Prevents data loss on timeouts/interruptions
• Dual console/file output for usability

4. Cost Integration

• Callback-based token tracking
• Per-technique granular analysis
• Session-based aggregation
• Real-time cost monitoring

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Special Security Considerations

• Technique 21 (Prompt Security): Implementation exists but output intentionally not generated to avoid demonstrating attack patterns that could be misused

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🤝 Contributing

Contributions are welcome! This project implements prompt engineering techniques as a learning resource and production template.

How to Contribute

1. Fork the repository
2. Create a feature branch (git checkout -b feature/improvement)
3. Implement your changes with tests
4. Commit with clear messages (git commit -m 'Add: new utility for X')
5. Push to your fork (git push origin feature/improvement)
6. Open a Pull Request with detailed description

Contribution Areas

• Additional techniques from emerging research
• Utility enhancements (new validators, optimizers)
• Documentation improvements (examples, tutorials)
• Performance optimizations (caching, batching)
• Alternative LLM providers (Anthropic, Cohere, open-source)

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• Original Research: Prompt engineering techniques derived from extensive research and best practices in the field
• LangChain Team: For the excellent framework enabling modern prompt engineering patterns
• OpenAI: For the powerful API and models making this work possible
• Community: For continuous prompt engineering research and innovation

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📧 Contact & Support

Issues: Please use the GitHub Issues page for bug reports and feature requests.

Questions: For implementation questions or discussions, open a GitHub Discussion.

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Built with ❤️ for the Prompt Engineering Community
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