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Course Outline
Best Practices and Tools
Common Pitfalls and Mitigation Strategies
Introduction to Prompt Engineering
Prompt Refinement and Iterative Design
Prompting for Test Automation and SQL Generation
Summary and Next Steps
Using Prompts for Code Explanation and Debugging
Writing Prompts for Code Generation
- Avoiding hallucinated code or security vulnerabilities
- Handling incomplete or ambiguous inputs
- Creating safe fallback prompts and guardrails
- Creating test cases from requirements or code
- Generating structured SQL queries from natural language
- Formatting outputs for integration into test suites
- Explaining legacy or unfamiliar code
- Prompting for logic walkthroughs or edge case analysis
- Finding and explaining bugs or inefficiencies
- Generating code from plain-language descriptions
- Controlling output format and programming language
- Working with complex logic or multiple functions
- Improving results through prompt chaining and feedback loops
- Error recovery and prompt tuning strategies
- Case studies in refinement for technical tasks
- Prompt libraries and reuse patterns
- Using prompt templates in VS Code or API-based workflows
- Evaluating prompt quality and performance in production use
- Understanding prompts, context, tokens, and models
- Prompt types: zero-shot, one-shot, few-shot
- Using system vs. user instructions in different APIs
Requirements
Audience
- Developers using LLMs in code generation or analysis
- Technical leads exploring AI tools in workflows
- Software professionals experimenting with LLM integrations
- Experience in software development or scripting
- Familiarity with common programming languages (e.g., Python, JavaScript, SQL)
- Basic understanding of large language models and AI tools like ChatGPT, Claude, or Copilot
7 Hours
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
