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Duration 7 hours
Course Outline
Best Practices and Tooling
Typical Challenges and Mitigation Tactics
Getting Started with Prompt Engineering
Prompt Refinement and Iterative Design
Prompting for Test Automation and SQL Generation
Recap and Future Directions
Leveraging Prompts for Code Explanation and Debugging
Crafting Prompts for Code Generation
- Preventing hallucinated code or security vulnerabilities
- Managing incomplete or ambiguous inputs
- Designing safe fallback prompts and guardrails
- Deriving test cases from requirements or existing code
- Creating structured SQL queries from natural language
- Structuring outputs for integration into test suites
- Explaining legacy or unfamiliar code
- Requesting logic walkthroughs or edge case analysis
- Identifying and explaining bugs or inefficiencies
- Generating code from plain-language descriptions
- Controlling output format and programming language
- Handling complex logic or multiple functions
- Enhancing results via prompt chaining and feedback loops
- Error recovery and prompt tuning strategies
- Case studies on refinement for technical tasks
- Prompt libraries and reuse patterns
- Using prompt templates in VS Code or API-based workflows
- Assessing prompt quality and performance in production
- Grasping prompts, context, tokens, and models
- Prompt types: zero-shot, one-shot, few-shot
- Differentiating system vs. user instructions across APIs
Requirements
Target Audience
- Developers utilizing LLMs for code generation or analysis
- Technical leads investigating AI tools in their workflows
- Software professionals experimenting with LLM integrations
- Practical experience in software development or scripting
- Familiarity with standard programming languages (e.g., Python, JavaScript, SQL)
- Foundational knowledge of large language models and AI tools like ChatGPT, Claude, or Copilot
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
