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 uses MDX files as the source of truth, enabling interactive code examples, embedded notebooks, and dynamic content rendering while maintaining a single source for all language variants through i18n middleware. Content is organized hierarchically across 745+ pages covering foundational to advanced prompting techniques.

Provides structured educational content explaining Chain-of-Thought prompting methodology, which breaks down complex reasoning tasks into intermediate steps. The guide documents the theoretical foundation, implementation patterns, and practical examples showing how CoT improves LLM accuracy on multi-step reasoning problems. Content includes worked examples demonstrating step-by-step reasoning decomposition.

Documents adversarial prompting attacks (prompt injection, jailbreaking, manipulation) and defense strategies to make LLM systems robust. The guide explains attack vectors like instruction override, context confusion, and output manipulation, along with defensive techniques like input validation, output filtering, and prompt hardening.

Provides structured documentation comparing LLM capabilities across providers (OpenAI, Anthropic, open-source) and architectures (GPT-4, Claude, Llama, etc.), covering performance characteristics, cost, context window, and specialized capabilities. The guide helps developers select appropriate models for specific use cases based on task requirements and constraints.

Documents function calling capabilities that enable LLMs to invoke external tools and APIs by generating structured function calls. The guide explains how to define function schemas, parse LLM function call outputs, handle execution results, and integrate function calling into agent loops for tool-augmented reasoning.

Documents context engineering practices for building effective AI agents, including how to structure system prompts, manage conversation history, implement memory systems, and handle context window constraints. The guide covers techniques for maintaining agent state, prioritizing relevant context, and designing prompts that enable agents to reason effectively within limited context windows.

Documents techniques for using LLMs to generate synthetic training data, evaluation datasets, and test cases. The guide covers prompt engineering for data generation, quality control strategies, and how to use synthetic data for fine-tuning, evaluation, and testing LLM applications.

Documents fine-tuning approaches for adapting LLMs to specific tasks, including when to fine-tune vs use prompt engineering, how to prepare training data, and how to combine fine-tuning with advanced prompting techniques. The guide covers fine-tuning for GPT-4o and discusses tradeoffs between fine-tuning and in-context learning.

Provides executable Jupyter notebooks demonstrating prompt engineering techniques with runnable code examples. Notebooks cover techniques like CoT, PAL, adversarial prompting, and RAG with actual LLM API calls, enabling learners to experiment and modify examples in real-time.

Curates and summarizes research papers on prompt engineering, RAG, LLM agents, and related topics, providing links to original papers and distilled summaries of key findings. The collection helps practitioners stay current with research advances and understand the theoretical foundations of prompting techniques.

Documents RAG methodology for augmenting LLM responses with retrieved external knowledge, explaining the three-stage pipeline: retrieval (finding relevant documents), augmentation (injecting context into prompts), and generation (LLM producing grounded responses). The guide covers architectural patterns for building RAG systems, including vector store integration, retrieval ranking strategies, and context window management.

Explains the ReAct framework that combines reasoning (chain-of-thought) with acting (tool use), enabling LLMs to iteratively think, act, and observe in a loop. The guide documents the ReAct prompt structure, how to integrate external tools/APIs, and how to manage the reasoning-action-observation cycle. Content shows how ReAct enables agents to solve complex tasks requiring multiple tool invocations.

Documents Tree of Thoughts methodology that explores multiple reasoning paths simultaneously rather than a single linear chain, enabling LLMs to backtrack and explore alternative solutions. The guide explains the ToT search strategy (breadth-first, depth-first, beam search), how to evaluate intermediate reasoning states, and when ToT outperforms simpler techniques like CoT.

Explains Self-Consistency methodology that samples multiple reasoning paths from an LLM and aggregates results through majority voting or weighted consensus, improving accuracy on reasoning tasks without requiring external tools. The technique leverages LLM temperature/sampling to generate diverse reasoning traces, then selects the most consistent answer across samples.

Documents Prompt Chaining methodology that breaks complex tasks into sequential prompts where outputs from one step feed into the next, enabling task decomposition and intermediate validation. The guide explains how to design prompt chains, manage context between steps, and handle errors in multi-step workflows.

Documents Automatic Prompt Engineer methodology that uses LLMs to generate and optimize prompts for specific tasks through iterative search and evaluation. APE treats prompt optimization as a search problem, generating candidate prompts, evaluating them on a task, and iteratively improving based on performance feedback.

Explains foundational prompting techniques where zero-shot uses no examples and few-shot provides a small number of examples to guide LLM behavior. The guide documents how examples improve task understanding, the importance of example selection and ordering, and when zero-shot vs few-shot is appropriate.

Documents Program-Aided Language Models technique where LLMs generate executable code (Python, etc.) to solve problems rather than reasoning purely in natural language. PAL leverages LLMs' code generation abilities to handle complex math, logic, and computation tasks by writing programs that can be executed for precise results.