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Duration 7 hours
Course Outline
Introduction to Prompt Engineering
Prompt Refinement and Iterative Design
Writing Prompts for Code Generation
Using Prompts for Code Explanation and Debugging
Prompting for Test Automation and SQL Generation
Common Pitfalls and Mitigation Strategies
Best Practices and Tools
Summary and Next Steps
- Grasping the fundamentals of prompts, context, tokens, and models.
- Distinguishing between zero-shot, one-shot, and few-shot prompting techniques.
- Differentiating system and user instructions across various API interfaces.
- Enhancing outcomes via prompt chaining and feedback mechanisms.
- Implementing error recovery and prompt tuning methodologies.
- Examining case studies on refining prompts for specific technical challenges.
- Translating plain-language descriptions into functional code.
- Regulating output formats and specifying programming languages.
- Managing complex logic structures or multi-function requirements.
- Clarifying legacy or unfamiliar codebases.
- Requesting logic walkthroughs or edge case analyses.
- Identifying and explaining bugs or performance inefficiencies.
- Deriving test cases from requirements or existing code.
- Generating structured SQL queries from natural language descriptions.
- Formatting outputs for seamless integration into test suites.
- Preventing hallucinated code or introducing security vulnerabilities.
- Managing incomplete or ambiguous user inputs.
- Designing safe fallback prompts and establishing guardrails.
- Utilizing prompt libraries and reusable patterns.
- Applying prompt templates within VS Code or API-driven workflows.
- Assessing prompt quality and performance in production settings.
Requirements
Audience
- Developers utilizing LLMs for code generation or analysis.
- Technical leaders investigating the integration of AI tools into their workflows.
- Software professionals experimenting with LLM integrations.
- Prior experience in software development or scripting.
- Proficiency in common programming languages such as Python, JavaScript, or SQL.
- A foundational understanding 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
