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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
- Addressing incomplete or ambiguous inputs
- Establishing safe fallback prompts and guardrails
- Deriving test cases from requirements or existing code
- Creating structured SQL queries from natural language descriptions
- Formatting outputs for seamless integration into test suites
- Decoding legacy or unfamiliar code
- Requesting logic walkthroughs or edge case analyses
- Identifying and explaining bugs or inefficiencies
- Generating code from plain-language descriptions
- Controlling output format and target programming language
- Managing complex logic or multi-function requirements
- Enhancing results via prompt chaining and feedback loops
- Error recovery and prompt tuning strategies
- Case studies on refinement for technical tasks
- Prompt libraries and reusable patterns
- Applying prompt templates in VS Code or API-based workflows
- Assessing prompt quality and performance in production settings
- Grasping prompts, context, tokens, and model mechanics
- Prompt types: zero-shot, one-shot, and few-shot
- Differentiating between system and user instructions across various APIs
Requirements
Target Audience
- Developers utilizing LLMs for code generation or analysis
- Technical leads investigating AI tools within their workflows
- Software professionals exploring LLM integrations
- Background in software development or scripting
- Familiarity with standard 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
