The article distinguishes between interactive and transactional prompting, arguing that prompt engineering is most valuable for transactional, objective tasks with LLMs.
Explores the difference between rigorous prompt engineering and amateur 'blind prompting' for language models, advocating for a systematic, test-driven approach.
Explores the Reflexion technique where LLMs like GPT-4 can critique and self-correct their own outputs, a potential new tool in prompt engineering.
An overview of prompt engineering techniques for large language models, including zero-shot and few-shot learning methods.
Explores AI image generation tools like DALL·E 2 and Stable Diffusion, and the emerging market for specialized prompts on platforms like Promptbase.
An engineer shares insights and tutorials on applying Cohere's large language models for real-world tasks like prompt engineering and semantic search.
