External Resources
Curated links to expand your prompt engineering and AI knowledge.
Official Documentation
OpenAI (ChatGPT)
Prompt Engineering Guide
https://platform.openai.com/docs/guides/prompt-engineering
Comprehensive guide from OpenAI on effective prompting techniques
Best Practices for ChatGPT
https://help.openai.com/en/articles/10032626-prompt-engineering-best-practices-for-chatgpt
Practical tips for day-to-day usage
Anthropic (Claude)
Prompt Library
https://docs.anthropic.com/claude/prompt-library
Ready-to-use prompts for common tasks
Prompt Engineering Guide
https://docs.anthropic.com/claude/docs/prompt-engineering
Anthropic's approach to effective prompting
AI Principles
https://ai.google/principles/
Google's ethical AI framework
Prompt Engineering Overview
https://cloud.google.com/discover/what-is-prompt-engineering
Introduction to prompt engineering concepts
Best Practices
https://cloud.google.com/blog/products/application-development/five-best-practices-for-prompt-engineering
Five key strategies from Google
IBM
Prompt Engineering Topics
https://www.ibm.com/think/topics/prompt-engineering
IBM's perspective on prompting techniques
2025 Prompt Engineering Guide
https://www.ibm.com/think/prompt-engineering
Comprehensive guide to mastering prompt engineering
Educational Resources
Prompt Engineering Guide
Website: https://www.promptingguide.ai/
Comprehensive, community-driven guide covering:
- Prompting techniques
- Applications across domains
- Models and tools
- Research papers
Techniques Section: https://www.promptingguide.ai/techniques
Deep dives into specific prompting strategies
Tools & Platforms
OpenAI Tokenizer
URL: https://platform.openai.com/tokenizer
Visualise how text is broken into tokens; estimate costs
Ollama (Local Models)
Website: https://ollama.com/
Run LLMs locally (used in our sandbox)
Model Library: https://ollama.com/library
Available models including Llama 3.3
Hugging Face
Website: https://huggingface.co/
Open-source models, datasets, and tools
Transformers Library: https://huggingface.co/docs/transformers/
For implementing NLMs and LLMs
Academic Papers & Research
Foundational Papers
"Attention Is All You Need" (2017)
Vaswani et al. - Introduced transformer architecture
https://arxiv.org/abs/1706.03762
"Language Models are Few-Shot Learners" (2020)
Brown et al. (GPT-3 paper)
https://arxiv.org/abs/2005.14165
Environmental Impact Research
"Making AI Less Thirsty" (2023)
Li et al. Water footprint of AI models
https://arxiv.org/abs/2304.03271
"Power Hungry Processing" (2024)
Energy costs of AI deployment
https://arxiv.org/abs/2311.16863
Ethics & Bias
Privacy and Security: - ICO Guide to AI and Data Protection
Algorithmic Justice League
Website: https://www.ajl.org/
Research and advocacy on bias in AI
Community & Discussion
r/PromptEngineering (Reddit)
URL: https://www.reddit.com/r/PromptEngineering/
Community sharing prompts and techniques
r/LocalLLaMA (Reddit)
URL: https://www.reddit.com/r/LocalLLaMA/
Discussion of running LLMs locally
Newsletters & Blogs
The Batch (DeepLearning.AI)
URL: https://www.deeplearning.ai/the-batch/
Weekly AI news and insights
Import AI
URL: https://jack-clark.net/
Weekly newsletter on AI developments
Local Sandbox
Access: http://192.168.1.177:3000
During workshop only
Course Contact
Instructor: Avgi Stavrou
Email: avgi.stavrou.22@gmail.com
Staying Current
AI is rapidly evolving. To stay up-to-date:
- Follow key researchers on Twitter/X: Andrej Karpathy, Yann LeCun, Andrew Ng
- Subscribe to newsletters: The Batch, Import AI
- Join communities: Reddit r/PromptEngineering, r/LocalLLaMA
- Read release notes: When tools you use release updates
- Experiment: Try new models and techniques in sandbox
- Share: Contribute your findings back to the community
Contributing to This Course
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