Sources & Further Reading
iSources checked: September 2026
The content of this module was researched in May 2026; all source links were checked for availability in September 2026 and updated where needed. As the AI landscape evolves quickly, some details may have changed since then.
Sources
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Wei, J. et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv:2201.11903. https://arxiv.org/abs/2201.11903
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Yao, S. et al. (2023). Tree of Thoughts: Deliberate Problem Solving with Large Language Models. arXiv:2305.10601. https://arxiv.org/abs/2305.10601
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Wang, X. et al. (2023). Self-Consistency Improves Chain of Thought Reasoning in Language Models. arXiv:2203.11171. https://arxiv.org/abs/2203.11171
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OpenAI (2024). Prompt Engineering Guide. https://developers.openai.com/api/docs/guides/prompt-engineering
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Anthropic (2024). Prompt Engineering Documentation. https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/overview
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OWASP (2025). OWASP Top 10 for LLM Applications. https://owasp.org/www-project-top-10-for-large-language-model-applications/
Further Reading
Learning Prompt Techniques
- Learn Prompting: Comprehensive open-source course on prompt engineering techniques. https://learnprompting.org/
- Anthropic Prompt Library: Collection of proven prompts for various use cases. https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices
Security & Prompt Injection
- Simon Willison – Prompt Injection: Ongoing collection of articles and analyses on prompt injection attacks. https://simonwillison.net/series/prompt-injection/
- OWASP LLM Top 10: Complete list of the most common security risks in LLM applications. https://owasp.org/www-project-top-10-for-large-language-model-applications/
Deep Dives
- Lilian Weng – Prompt Engineering: Detailed overview of prompting strategies and their scientific foundations. https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/