Generative AI: Prompt Engineering for Software Developers

In the Generative AI: Prompt Engineering for Software Developers course, you'll learn:

Using Large Language Models (LLMs)

• Understand model capabilities and limitations
• Format and preprocess input data for LLMs
• Optimize prompt design for accuracy and efficiency
• Apply retrieval-augmented generation (RAG) for contextual responses
• Implement ethical safeguards and bias mitigation
• Handle errors, manage API usage, and optimize costs
• Monitor model performance and adjust strategies accordingly

Writing and Iterating on Prompts using OpenAI API

• Define clear goals and expected outputs
• Craft specific, structured prompts
• Experiment with variations and control outputs
• Leverage API parameters (temperature, max tokens, system messages)
• Manage conversation context across multiple interactions
• Review and refine prompts based on model behavior
• Implement bias reduction techniques and responsible AI usage
• Stay informed on evolving LLM capabilities

Building Custom Chatbots

• Define purpose and audience
• Choose a development platform
• Design conversation flows with context retention and multi-turn interactions
• Implement retrieval-augmented generation (RAG) for better responses
• Integrate with communication channels
• Monitor performance, optimize prompt engineering, and manage errors
• Handle common queries effectively

Best Practices for LLM-Supported App Development

• Optimize input data to improve LLM performance
•  Use prompt engineering and retrieval-based techniques before fine-tuning
• Design prompts that balance creativity, accuracy, and efficiency
• Implement security best practices for API-based LLMs
• Optimize latency and cost efficiency for real-time applications
• Set ethical guidelines to prevent bias or harm
• Monitor and review LLM-generated content