Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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
- Establishing safe fallback prompts and guardrails
- Deriving test cases from requirements or existing code
- Creating structured SQL queries from natural language
- Structuring outputs for integration into test suites
- Clarifying legacy or unfamiliar code
- Requesting logic walkthroughs or edge case analysis
- Identifying and explaining bugs or inefficiencies
- Producing code from plain-language descriptions
- Controlling output format and programming language
- Handling complex logic or multiple functions
- Enhancing results via prompt chaining and feedback loops
- Strategies for error recovery and prompt tuning
- 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 prompts, context, tokens, and models
- Prompt types: zero-shot, one-shot, few-shot
- Using system versus user instructions across different APIs
Requirements
Target Audience
- Developers leveraging LLMs for code generation or analysis
- Technical leads investigating AI tools within their workflows
- Software professionals testing LLM integrations
- Practical experience in software development or scripting
- Familiarity with mainstream programming languages (e.g., Python, JavaScript, SQL)
- Foundational knowledge 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
