Sources

Authoritative Sources

The definitions, techniques, and practices in this pack are grounded in the following primary and widely recognized sources: official model-provider documentation, research papers that introduced key techniques, and community security standards. Where a technique has a canonical originating paper, that paper is cited alongside the maintained vendor guidance.

Model Provider Documentation

  • Anthropic - Prompt engineering overview (Claude Docs). https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
  • Anthropic - Use XML tags to structure prompts. https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/use-xml-tags
  • Anthropic - Extended thinking. https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking
  • Anthropic - Prompt caching. https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching
  • Anthropic - Tool use (function calling). https://docs.anthropic.com/en/docs/build-with-claude/tool-use/overview
  • Anthropic - Reducing hallucinations. https://docs.anthropic.com/en/docs/test-and-evaluate/strengthen-guardrails/reduce-hallucinations
  • Anthropic - Building effective agents. https://www.anthropic.com/engineering/building-effective-agents
  • Anthropic - Effective context engineering for AI agents. https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
  • OpenAI - Prompt engineering guide (API docs). https://platform.openai.com/docs/guides/prompt-engineering
  • OpenAI - Structured Outputs. https://platform.openai.com/docs/guides/structured-outputs
  • Google - Gemini API prompting strategies. https://ai.google.dev/gemini-api/docs/prompting-strategies

Foundational Research Papers

  • Brown et al. (2020) - Language Models are Few-Shot Learners (GPT-3). https://arxiv.org/abs/2005.14165
  • Wei et al. (2022) - Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. https://arxiv.org/abs/2201.11903
  • Kojima et al. (2022) - Large Language Models are Zero-Shot Reasoners ("Let's think step by step"). https://arxiv.org/abs/2205.11916
  • Wang et al. (2022) - Self-Consistency Improves Chain of Thought Reasoning. https://arxiv.org/abs/2203.11171
  • Yao et al. (2022) - ReAct: Synergizing Reasoning and Acting in Language Models. https://arxiv.org/abs/2210.03629
  • Lewis et al. (2020) - Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. https://arxiv.org/abs/2005.11401
  • Bai et al. (2022) - Constitutional AI: Harmlessness from AI Feedback. https://arxiv.org/abs/2212.08073
  • Zheng et al. (2023) - Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. https://arxiv.org/abs/2306.05685

Community Guides and Standards

  • DAIR.AI - Prompt Engineering Guide. https://www.promptingguide.ai/
  • OWASP - Top 10 for Large Language Model Applications. https://owasp.org/www-project-top-10-for-large-language-model-applications/
  • Model Context Protocol - Specification and documentation. https://modelcontextprotocol.io/
  • RAGAS - Evaluation framework for RAG pipelines (documentation). https://docs.ragas.io/