Prompt_Engineering

Teaches and implements zero-shot prompting by providing Jupyter notebook tutorials that demonstrate how to craft single-turn prompts without examples, using clear instruction structures and role definitions. The implementation uses OpenAI and Claude APIs with templated prompt patterns that guide LLMs to perform tasks based solely on task description and context, without requiring few-shot examples or chain-of-thought reasoning.

Implements few-shot prompting by providing Jupyter tutorials that demonstrate how to include 2-5 labeled examples in prompts to guide LLM behavior through demonstration rather than explicit instruction. The approach uses OpenAI/Claude APIs with structured example formatting, showing how to select representative examples, format them consistently, and measure their impact on model output quality and consistency.

Teaches negative prompting through Jupyter notebooks that demonstrate how to explicitly specify what the model should NOT do or produce, improving output quality by excluding unwanted behaviors. The approach uses OpenAI/Claude APIs with patterns like 'Do not include X' or 'Avoid Y' to guide models away from common failure modes, hallucinations, or undesired output characteristics. Includes techniques for identifying effective negative constraints.

Implements prompt formatting through Jupyter notebooks that teach how to structure prompts and specify output formats (JSON, markdown, tables, code) to ensure consistent, parseable results. The approach uses OpenAI/Claude APIs with explicit format directives and examples to guide models toward structured outputs, enabling downstream processing and integration with other systems. Includes validation patterns to verify output format compliance.

Teaches multilingual prompting through Jupyter notebooks that demonstrate how to craft prompts for non-English languages and handle cross-language tasks (translation, multilingual reasoning, code-switching). The approach uses OpenAI/Claude APIs to show language-specific prompt patterns, handling of character encodings, and techniques for maintaining consistency across languages. Includes guidance on when to use native language vs English for better model performance.

Implements ethical prompting through Jupyter notebooks that teach how to design prompts that reduce bias, avoid harmful outputs, and align with ethical principles. The approach uses OpenAI/Claude APIs to demonstrate bias detection in prompts, techniques for neutral language, and methods for evaluating fairness and safety in outputs. Includes patterns for responsible AI practices in prompt design.

Teaches prompt security through Jupyter notebooks that demonstrate how to design prompts resistant to adversarial attacks, prompt injection, and jailbreaking attempts. The approach uses OpenAI/Claude APIs to show common attack patterns, defensive prompt structures, and validation techniques to prevent misuse. Includes patterns for input sanitization, output validation, and detecting suspicious requests.

Implements prompt evaluation through Jupyter notebooks that teach how to measure prompt quality using metrics (accuracy, consistency, relevance), benchmarks, and test datasets. The approach uses OpenAI/Claude APIs to generate outputs, compare against ground truth or quality criteria, and quantify improvements. Includes techniques for designing evaluation frameworks and interpreting results across different models and tasks.

Teaches LangChain integration through Jupyter notebooks that demonstrate how to use LangChain's prompt templates, chains, and agents to orchestrate complex prompting workflows. The approach uses LangChain abstractions (PromptTemplate, LLMChain, agents) with OpenAI/Claude APIs to simplify multi-step prompting, variable substitution, and output parsing. Includes patterns for building reusable prompt components and managing state across chain steps.

Implements OpenAI API integration through Jupyter notebooks that teach how to use OpenAI's API effectively for prompting, including authentication, model selection, parameter tuning (temperature, max_tokens), and error handling. The approach uses the OpenAI Python client library with patterns for managing API keys, handling rate limits, and optimizing costs. Includes best practices for production deployments.

Teaches chain-of-thought (CoT) prompting through Jupyter notebooks that demonstrate how to structure prompts to make LLMs verbalize intermediate reasoning steps before producing final answers. The implementation uses explicit prompt patterns like 'Let's think step by step' and shows how to parse multi-step reasoning outputs, enabling better performance on complex reasoning, math, and logic tasks by forcing the model to show its work.

Implements self-consistency prompting through Jupyter notebooks that demonstrate generating multiple independent reasoning chains for the same problem and selecting the most common final answer via majority voting. The approach uses OpenAI/Claude APIs to generate N diverse CoT outputs, then aggregates them to improve accuracy beyond single-chain reasoning, particularly effective for math and logic problems where multiple valid reasoning paths exist.

Teaches role-based prompting through Jupyter notebooks that demonstrate how to inject personas or expert roles into prompts to guide LLM behavior and output style. The implementation uses patterns like 'You are a senior software architect' or 'Act as a data scientist' to prime the model toward specific expertise levels, communication styles, and domain knowledge, improving output relevance and quality for specialized tasks.

Implements task decomposition through Jupyter notebooks that teach how to break complex problems into sequential sub-tasks, each with its own prompt, and chain the outputs together. The approach uses OpenAI/Claude APIs to execute multi-step workflows where output from one prompt feeds into the next, enabling complex reasoning, content generation, and problem-solving by reducing each step's complexity.

Teaches instruction engineering through Jupyter notebooks that demonstrate how to write precise, unambiguous instructions that guide LLM behavior toward specific outputs. The implementation covers constraint specification (output format, length, style), negative instructions (what NOT to do), and structured directives that reduce ambiguity and improve output consistency. Uses OpenAI/Claude APIs with examples of instruction clarity improvements.

Implements prompt optimization through Jupyter notebooks that teach systematic approaches to improving prompts through iteration, testing, and measurement. The approach uses OpenAI/Claude APIs to generate outputs, measure quality against criteria (accuracy, format compliance, style), and iteratively refine prompts based on results. Includes techniques for A/B testing prompt variations and identifying which changes improve performance.

Teaches ambiguity handling through Jupyter notebooks that demonstrate how to identify and resolve ambiguous language in prompts that could lead to multiple interpretations. The approach uses OpenAI/Claude APIs to show how clarifying questions, context provision, and explicit scope definition reduce ambiguity and improve output consistency. Includes patterns for detecting when a prompt is ambiguous and techniques for making it more precise.

Implements prompt length optimization through Jupyter notebooks that teach how to balance prompt detail with token efficiency and model performance. The approach uses OpenAI/Claude APIs to demonstrate how longer prompts with more context improve accuracy but increase costs and latency, while shorter prompts reduce costs but may lose important context. Includes techniques for identifying essential vs redundant information and strategies for compression.