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Duration 7 hours
Course Outline
Best Practices and Essential Tools
Common Challenges and Mitigation Approaches
Overview of Prompt Engineering
Prompt Refinement and Iterative Design Processes
Prompting Strategies for Test Automation and SQL Generation
Key Takeaways 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 effectively
- Establishing safe fallback prompts and robust guardrails
- Deriving test cases from requirements or existing code
- Translating natural language into structured SQL queries
- Structuring outputs for seamless integration into test suites
- Interpreting legacy or unfamiliar code segments
- Requesting logic walkthroughs or edge case analysis via prompts
- Identifying and elucidating bugs or performance inefficiencies
- Generating code from plain-language descriptions
- Controlling output formatting and target programming language
- Handling complex logic or multi-function interactions
- Enhancing outcomes through prompt chaining and feedback loops
- Strategies for error recovery and prompt tuning
- Examining case studies on refinement for technical tasks
- Utilizing prompt libraries and reusable patterns
- Applying prompt templates within VS Code or API-based workflows
- Assessing prompt quality and performance in production environments
- Grasping the fundamentals of prompts, context, tokens, and models
- Distinguishing prompt types: zero-shot, one-shot, and few-shot
- Differentiating between system and user instructions across various APIs
Requirements
Target Audience
- Developers leveraging LLMs for code generation or analysis
- Technical leaders investigating the integration of AI tools into their workflows
- Software experts exploring LLM integrations
- Practical experience in software development or scripting
- Knowledge of mainstream 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
