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Duration 7 hours
Course Outline
Established Best Practices and Supporting Tools
Identification of Common Pitfalls and Risk Mitigation Strategies
Introduction to Prompt Engineering Principles
Iterative Prompt Refinement and Design Methodologies
Application of Prompts in Test Automation and SQL Query Generation
Summary and Recommended Future Actions
Utilizing Prompts for Code Explanation and Debugging Processes
Developing Prompts for Effective Code Generation
- Preventing hallucinated code outputs and mitigating security vulnerabilities
- Managing incomplete or ambiguous input data
- Establishing safe fallback prompts and operational guardrails
- Deriving test cases from functional requirements or existing code
- Generating structured SQL queries from natural language descriptions
- Formatting outputs for seamless integration into automated test suites
- Analyzing legacy or unfamiliar codebases
- Soliciting logic walkthroughs and edge case analysis
- Identifying and explaining bugs or performance inefficiencies
- Generating code from plain-language functional specifications
- Controlling output formats and specific programming languages
- Managing complex logic structures and multi-function interactions
- Enhancing outcomes through prompt chaining and iterative feedback loops
- Implementing error recovery protocols and prompt tuning techniques
- Reviewing case studies on refinement for specialized technical tasks
- Leveraging prompt libraries and standardized reuse patterns
- Applying prompt templates in VS Code or API-based development workflows
- Evaluating prompt quality and performance in production environments
- Understanding fundamental concepts: prompts, context, tokens, and model architecture
- Distinguishing prompt types: zero-shot, one-shot, and few-shot
- Differentiating system versus user instructions across various API platforms
Requirements
Target Audience
- Developers utilizing LLMs for code generation or analysis
- Technical leads evaluating AI tools for workflow integration
- Software professionals exploring LLM integration capabilities
- Professional experience in software development or scripting
- Proficiency in common programming languages (e.g., Python, JavaScript, SQL)
- 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
