References
Over the past two years, I have read dozens of papers, guides, courses, and blogs to learn, understand, and improve my prompting skills. The references below can greatly assist you in gaining a deeper understanding of what it takes to master the art of AI prompt creation.
[1] Schulhoff, S. et al. (2024). "The Impact of Prompt Quality on Large Language Model Performance." Proceedings of the Conference on Empirical Methods in Natural Language Processing.
[2] Hoffman, R. (2024). "Improving Human Potential with AI." Harvard Business Review, March 2024.
[3] Lakera AI Research Team. (2025). "Comprehensive Guide to Prompt Engineering Techniques." Lakera AI Blog. https://www.lakera.ai/blog/prompt-engineering-guide
[4] Schulhoff, S. (2025). "AI Prompt Engineering in 2025: What Works and What Doesn't." Lenny's Newsletter. https://www.lennysnewsletter.com/p/ai-prompt-engineering-in-2025-sander-schulhoff
[5] K2view Research Team. (2025). "Prompt Engineering Techniques: Top 5 for 2025." K2view Blog. https://www.k2view.com/blog/prompt-engineering-techniques/
[6] Wei, J. et al. (2022). "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models." Advances in Neural Information Processing Systems.
[7] Comprehensive Prompting Guide. (2025). "Advanced Prompting Techniques." Prompting Guide AI. https://www.promptingguide.ai/
[8] Collins, B. (2024). "I Cracked Google's Prompt Engineering Playbook." Medium. https://medium.com/@bryanjcollins/i-cracked-googles-prompt-engineering-playbook-60897ddd4316
[9] PromptHub Team. (2025). "Google's New Prompt Engineering Guide." PromptHub Substack. https://prompthub.substack.com/p/googles-new-prompt-engineering-guide
[10] Google AI Research Team. (2024). "Best Practices for Prompt Engineering: A Four-Part Framework." Google AI Blog.
[11] Dudhat, L. (2025). "AI Prompt Engineering Use Cases Driving Business Innovation." SolGuruz Blog. https://solguruz.com/blog/ai-prompt-engineering-use-cases/
[12] Brown, T. et al. (2020). "Language Models are Few-Shot Learners." Advances in Neural Information Processing Systems.
[13] Medical Coding Startup Case Study. (2024). "Improving Medical Coding Accuracy with Advanced Prompt Engineering." Healthcare AI Journal.
[14] Kojima, T. et al. (2022). "Large Language Models are Zero-Shot Reasoners." Advances in Neural Information Processing Systems.
[15] Wang, X. et al. (2022). "Self-Consistency Improves Chain of Thought Reasoning in Language Models." International Conference on Learning Representations.
[16] Zhou, D. et al. (2022). "Least-to-Most Prompting Enables Complex Reasoning in Large Language Models." International Conference on Learning Representations.
[17] Madaan, A. et al. (2023). "Self-Refine: Iterative Refinement with Self-Feedback." Advances in Neural Information Processing Systems.
[18] OpenAI Research Team. (2024). "ChatGPT Technical Report." OpenAI Blog.
[19] Anthropic Safety Team. (2024). "Constitutional AI and Prompt Injection Defense." Anthropic Research.
[20] Google DeepMind. (2025). "Reasoning Models: The Next Generation of AI Systems." DeepMind Blog.
[21] Yao, S. et al. (2023). "ReAct: Synergizing Reasoning and Acting in Language Models." International Conference on Learning Representations.
[22] Li, J. et al. (2023). "BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models." International Conference on Machine Learning.
[23] Zhou, Y. et al. (2023). "Large Language Models Are Human-Level Prompt Engineers." International Conference on Learning Representations.
[24] Industry AI Applications Research Consortium. (2025). "Specialized AI Assistants: Industry-Specific Applications and Best Practices."
[25] Future of Work Institute. (2025). "Human-AI Collaboration: Emerging Patterns and Best Practices." MIT Technology Review.
This Missing Guide to Prompt Engineering represents the current state of prompt engineering knowledge as of June 2025. The field evolves rapidly, and techniques may change as new models and capabilities emerge. For the latest updates and community discussions, visit the resources listed in the Resources section.
About the Author: This Missing Guide to Prompt Engineering was created by Ishwar Jha, drawing inspiration from the learning outcomes of several courses, extensive research of current prompt engineering literature, expert interviews, and practical applications across multiple industry domains.
Most prompt examples are our experiments while building products and solutions for our clients at Appetals Solutions.