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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.
- Establishing 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 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 through prompt chaining and feedback loops.
- Error recovery and prompt tuning strategies.
- Case studies on refinement for technical tasks.
- Prompt libraries and reuse patterns.
- Utilizing prompt templates in VS Code or API-based workflows.
- Assessing prompt quality and performance in production use.
- Grasping prompts, context, tokens, and models.
- Prompt types: zero-shot, one-shot, few-shot.
- Applying system vs. user instructions across different APIs.
Requirements
Audience
- Developers leveraging LLMs for code generation or analysis.
- Technical leads exploring the integration of AI tools into their workflows.
- Software professionals experimenting with LLM integrations.
- Experience in software development or scripting.
- Familiarity with common programming languages (e.g., Python, JavaScript, SQL).
- A foundational understanding 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
