A summary of Chapter 6 from 'Prompt Engineering for LLMs', covering prompt structure, document templates, and strategies for effective context inclusion.
Explores key prompt engineering techniques like zero-shot, few-shot, and chain-of-thought to improve generative AI outputs.
Practical lessons from integrating LLMs into a product, focusing on prompt design pitfalls like over-specification and handling null responses.
Explains core prompting fundamentals for effective LLM use, including mental models, role assignment, and practical workflow with examples.
A practical guide sharing lessons learned from a year of building real-world applications with Large Language Models (LLMs).
Analysis of LLM wrapper libraries like LangChain and Guardrails, examining their hidden prompts and API call efficiency for structured JSON output.
Explores strategies and Azure OpenAI features to mitigate inappropriate use and enhance safety in AI chatbot implementations.
A curated list of resources for learning Generative AI and Prompt Engineering, including guides, tutorials, and documentation from OpenAI, DeepLearning.AI, and Microsoft.
Learn how Amazon CodeWhisperer uses generative AI to convert natural language into SQL code, accelerating database development.
Learn best practices and tips for using Amazon CodeWhisperer, a generative AI coding companion, to accelerate software development.
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.
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.
