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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 the generation of hallucinated code or security vulnerabilities.
- Managing incomplete or ambiguous inputs.
- Designing safe fallback prompts and guardrails.
- Deriving test cases from requirements or existing code.
- Converting natural language into structured SQL queries.
- Formatting outputs for seamless integration into test suites.
- Explaining legacy or unfamiliar codebases.
- Prompting for logic walkthroughs and edge case analysis.
- Identifying and explaining bugs or performance inefficiencies.
- Generating code from plain-language descriptions.
- Controlling output format and selecting the appropriate programming language.
- Handling complex logic or multiple functions.
- Enhancing results via prompt chaining and feedback loops.
- Implementing error recovery and prompt tuning strategies.
- Reviewing case studies on refinement for technical tasks.
- Utilizing prompt libraries and reuse patterns.
- Employing prompt templates in VS Code or API-based workflows.
- Assessing prompt quality and performance in production environments.
- Grasping concepts such as prompts, context, tokens, and models.
- Understanding prompt types: zero-shot, one-shot, and few-shot.
- Applying system versus user instructions across different APIs.
Requirements
Audience
- Developers utilizing 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 such as Python, JavaScript, or 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
