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Duration 7 hours
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
- 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
- Formatting outputs for seamless integration into test suites
- Explaining legacy or unfamiliar code
- Requesting logic walkthroughs or edge case analyses
- Identifying and explaining bugs or inefficiencies
- Generating code from plain-language descriptions
- Directing output format and programming language
- Handling complex logic or multiple functions
- Enhancing results through prompt chaining and feedback loops
- Error recovery and prompt tuning strategies
- Case studies on refinement for technical tasks
- Prompt libraries and reuse patterns
- Implementing prompt templates in VS Code or API-based workflows
- Assessing prompt quality and performance in production environments
- Understanding prompts, context, tokens, and models
- Prompt types: zero-shot, one-shot, few-shot
- Utilising system versus user instructions across different APIs
Requirements
Target Audience
- Developers utilising LLMs for code generation or analysis
- Technical leads investigating AI tools within their workflows
- Software professionals exploring LLM integrations
- Background in software development or scripting
- Proficiency with common programming languages (e.g., Python, JavaScript, SQL)
- Foundational knowledge of large language models and AI tools such as 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
