Prompt Engineering Guide

Serves comprehensive prompt engineering educational content across 11 languages using Next.js 13 with Nextra 2.13 static site generation. The platform implements a middleware-based internationalization system that routes users to language-specific content (e.g., pages/introduction/basics.en.mdx, pages/introduction/basics.ar.mdx) with automatic language detection and manual override capabilities. Content is organized hierarchically through _meta.json files that define navigation structure per language, enabling consistent UX across locales while maintaining independent content management.

Provides comprehensive documentation of 15+ prompting techniques (Zero-Shot, Few-Shot, Chain-of-Thought, Tree of Thoughts, ReAct, RAG, PAL, Self-Consistency, Prompt Chaining, APE) organized as MDX pages with embedded PNG diagrams illustrating technique workflows. Each technique page includes conceptual explanation, implementation patterns, code examples, and visual architecture diagrams (e.g., img/ape-zero-shot-cot.png, img/active-prompt.png) that show how techniques compose with LLM inference. The documentation structure enables cross-referencing between techniques and provides practical guidance on when to apply each approach.

Documents fine-tuning approaches for customizing LLMs (e.g., GPT-4o fine-tuning) with guidance on when fine-tuning is appropriate vs. prompt engineering, data preparation strategies, and evaluation metrics. The guide covers training data requirements, cost-benefit analysis, and how to combine fine-tuning with prompt engineering for optimal results. It includes examples of fine-tuning for domain-specific tasks and comparison with few-shot prompting effectiveness.

Documents techniques for using LLMs to generate synthetic training data, including prompt engineering patterns for data generation, quality control strategies, and diversity mechanisms. The guide covers how to structure generation prompts to produce varied, high-quality synthetic examples, validation approaches to ensure synthetic data quality, and use cases where synthetic data is most effective (e.g., data augmentation, privacy-preserving datasets). Includes examples of generating synthetic datasets for classification, NER, and other NLP tasks.

Documents agent design patterns and context engineering strategies for building autonomous LLM agents, including agent framework components (planning, reasoning, tool use), context management for agents, and patterns for agent-environment interaction. The guide covers how to structure agent prompts for effective reasoning, manage context across multiple agent steps, and design agent workflows. It includes examples of ReAct agents, planning-based agents, and hierarchical agent architectures.

Documents techniques for identifying and mitigating biases in LLM-generated content, including bias categories (gender, racial, cultural), detection strategies through prompting, and mitigation patterns. The guide covers how to structure prompts to reduce bias, validate outputs for bias, and implement fairness checks. It includes examples of biased outputs, detection prompts, and mitigation strategies for different bias types.

Documents prompt chaining techniques for decomposing complex tasks into sequences of LLM calls, including workflow design patterns, context passing between steps, and error handling strategies. The guide covers how to structure individual prompts in a chain, manage outputs from one step as inputs to the next, and handle failures in multi-step workflows. It includes examples of chaining for complex reasoning tasks, content generation pipelines, and data processing workflows.

Provides executable Jupyter notebooks (pe-chatgpt-adversarial.ipynb, pe-pal.ipynb) demonstrating prompt engineering techniques with live code examples that can be run in Colab or local environments. Notebooks include step-by-step implementation of techniques like Program-Aided Language Models (PAL) and adversarial prompting, with actual API calls to LLMs, output examples, and explanations of results. This enables hands-on learning where practitioners can modify prompts, observe LLM responses, and experiment with parameter variations in real-time.

Maintains comprehensive documentation of LLM models (GPT-4, ChatGPT, open-source models) with detailed parameter explanations (temperature, top_p, frequency_penalty, etc.), context window sizes, cost information, and capability matrices. Documentation is organized by model family (OpenAI, open-source) and includes guidance on when to use each parameter setting and how parameter choices affect prompt engineering effectiveness. The reference includes model-specific features like context caching (Gemini), fine-tuning capabilities (GPT-4o), and function calling support across providers.

Curates and synthesizes findings from peer-reviewed prompt engineering research papers organized by topic (RAG, LLM Agents, prompting techniques). The collection includes paper summaries, key findings, and connections to practical techniques documented in the guide. This bridges academic research and practical application by showing how research insights (e.g., Chain-of-Thought effectiveness from Wei et al.) translate into actionable prompting strategies. The architecture enables cross-referencing between research findings and technique documentation.

Documents adversarial prompting techniques (jailbreaks, prompt injection, prompt leaking) with examples of how to attack LLM systems and defensive strategies to mitigate risks. The guide includes categorized attack patterns (e.g., role-playing jailbreaks, encoding-based attacks), code examples showing actual attack implementations, and defensive prompt engineering patterns. This enables security-conscious teams to evaluate their LLM systems' robustness and understand attack surface before deployment.

Documents real-world applications of prompt engineering across domains (ChatGPT conversational applications, code generation, synthetic data generation, workplace case studies) with concrete examples showing how techniques apply to specific problems. Each use case includes problem statement, prompting approach, code examples, and results. The library demonstrates technique composition (e.g., using CoT for code generation, RAG for domain-specific QA) and shows how to adapt techniques for different domains.

Documents techniques for improving LLM output reliability and factuality, including self-consistency prompting, verification strategies, and bias mitigation approaches. The guide covers how to structure prompts to reduce hallucinations, detect factual errors, and improve consistency across multiple generations. It includes practical patterns like generating multiple outputs and voting, fact-checking prompts, and domain-specific validation approaches.

Documents how to use function calling (tool calling) across different LLM providers (OpenAI, Anthropic, Gemini) with schema-based function definitions, parameter handling, and integration patterns. The documentation includes examples of defining function schemas, handling function responses, and chaining multiple function calls. It covers provider-specific differences in function calling APIs and shows how to structure prompts to encourage appropriate function usage.

Documents context caching techniques (e.g., Gemini's context caching feature) and optimization strategies for managing large context windows efficiently. The guide covers how to structure prompts for caching, cache invalidation patterns, and cost-benefit analysis of caching vs. re-processing context. It includes examples of caching long documents, system prompts, and few-shot examples to reduce API costs and latency for repeated queries over the same context.