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
- Developing test cases from requirements or code
- Generating structured SQL queries from natural language
- Structuring outputs for integration into test suites
- Clarifying legacy or unfamiliar code
- Prompting for logic walkthroughs or edge case analysis
- Identifying and explaining bugs or inefficiencies
- Generating code from plain-language descriptions
- Managing output format and programming language
- Working with complex logic or multiple functions
- Enhancing results through prompt chaining and feedback loops
- Error recovery and prompt tuning strategies
- Case studies in 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 use
- Grasping prompts, context, tokens, and models
- Prompt types: zero-shot, one-shot, few-shot
- Applying system vs. user instructions in different APIs
Requirements
Audience
- Developers utilizing LLMs for code generation or analysis
- Technical leads evaluating AI tools for workflow integration
- Software professionals exploring LLM integrations
- Experience in software development or scripting
- Proficiency with common programming languages (e.g., Python, JavaScript, SQL)
- Foundational knowledge of large language models and AI tools such as ChatGPT, Claude, or Copilot
Testimonials (2)
The session was highly interactive and applicable to the business.
Jorge Boscan - Chevron Global Technology Services Company
Course - Advanced GitHub Copilot & AI for Projects and Infrastructure
Machine Translated
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
