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Duration 7 hours
Course Outline
Best Practices and Tooling
Addressing Common Pitfalls and Mitigation Strategies
Foundational Concepts of Prompt Engineering
Iterative Design and Prompt Refinement
Prompting for Test Automation and SQL Generation
Summary and Recommended Next Steps
Applying Prompts for Code Explanation and Debugging
Crafting Prompts for Code Generation
- Preventing hallucinated code and potential security vulnerabilities
- Managing incomplete or ambiguous input data
- Implementing safe fallback prompts and protective guardrails
- Deriving test cases from requirements or existing code
- Translating natural language into structured SQL queries
- Formatting outputs for seamless integration into test suites
- Clarifying legacy or unfamiliar codebases
- Requesting logic walkthroughs and edge case analysis
- Identifying and explaining bugs or performance inefficiencies
- Generating code from plain-language specifications
- Controlling output structure and selecting specific programming languages
- Handling complex logic and multi-function implementations
- Enhancing results via prompt chaining and feedback loops
- Error recovery techniques and prompt tuning strategies
- Case studies on refinement for specific technical tasks
- Prompt libraries and reusable patterns
- Integrating prompt templates in VS Code or API-driven workflows
- Assessing prompt quality and performance in production environments
- Understanding the mechanics of prompts, context, tokens, and models
- Exploring prompt types: zero-shot, one-shot, and few-shot
- Differentiating system vs. user instructions across various APIs
Requirements
Target Audience
- Developers utilizing LLMs for code generation or analysis tasks
- Technical leads evaluating AI tools for workflow integration
- Software professionals exploring LLM-based integrations
- Background in software development or scripting
- Proficiency with common programming languages (e.g., Python, JavaScript, SQL)
- Fundamental knowledge of large language models and AI assistants 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
