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Duration 7 hours
Course Outline
Best Practices and Essential Tools
Common Challenges and Mitigation Strategies
Fundamentals of Prompt Engineering
Prompt Refinement and Iterative Design
Prompting for Test Automation and SQL Generation
Conclusion and Future Directions
Utilizing Prompts for Code Explanation and Debugging
Crafting Prompts for Code Generation
- Preventing hallucinated code or potential security vulnerabilities
- Managing incomplete or ambiguous inputs
- Designing safe fallback prompts and guardrails
- Deriving test cases from requirements or existing code
- Translating natural language into structured SQL queries
- Formatting outputs for seamless integration into test suites
- Analyzing legacy or unfamiliar codebases
- Requesting logic walkthroughs or edge case analyses
- Identifying and explaining bugs or performance inefficiencies
- Generating code from plain-language descriptions
- Controlling output format and specifying the programming language
- Handling complex logic or multi-function structures
- Enhancing outcomes through prompt chaining and feedback loops
- Implementing error recovery and prompt tuning techniques
- Examining case studies focused on refinement for technical tasks
- Leveraging prompt libraries and reuse patterns
- Applying prompt templates in VS Code or API-based workflows
- Assessing prompt quality and performance in production scenarios
- Understanding key concepts: prompts, context, tokens, and models
- Differentiating prompt types: zero-shot, one-shot, and few-shot
- Distinguishing between system and user instructions across various APIs
Requirements
Target Audience
- Developers leveraging LLMs for code generation or analysis
- Technical leads investigating the integration of AI tools into their workflows
- Software professionals exploring LLM integrations
- Practical experience in software development or scripting
- Proficiency in 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
