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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
- Designing safe fallback prompts and guardrails
- Deriving test cases from requirements or existing code
- Crafting structured SQL queries from natural language
- Structuring outputs for seamless integration into test suites
- Clarifying legacy or unfamiliar code
- Requesting logic walkthroughs or edge case analysis
- Identifying and explaining bugs or inefficiencies
- Generating code from plain-language descriptions
- Controlling output format and programming language selection
- Handling complex logic or multiple functions
- Enhancing results via prompt chaining and feedback loops
- Error recovery and prompt tuning techniques
- 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 environments
- Grasping the basics of prompts, context, tokens, and models
- Prompt types: zero-shot, one-shot, and few-shot
- Differentiating between system and user instructions across various APIs
Requirements
Audience
- Developers utilizing LLMs for code generation or analysis
- Technical leads evaluating AI tools within their workflows
- Software professionals experimenting with LLM integrations
- Proficiency in software development or scripting
- Knowledge of prevalent 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
