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Duration 7 hours
Course Outline
Best Practices and Essential Tools
Common Pitfalls and Strategies for Mitigation
Introduction to Prompt Engineering
Prompt Refinement and Iterative Design
Prompting for Test Automation and SQL Generation
Summary and Next Steps
Utilizing Prompts for Code Explanation and Debugging
Crafting 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 descriptions
- Structuring outputs for seamless integration into test suites
- Clarifying legacy or unfamiliar codebases
- Requesting logic walkthroughs or edge-case analysis
- Identifying and explaining bugs or performance inefficiencies
- Generating code from plain-language descriptions
- Controlling output format and target programming language
- Handling complex logic or multiple functions
- Enhancing results via prompt chaining and feedback loops
- Implementing error recovery and prompt tuning strategies
- Reviewing 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
- Understanding the mechanics of 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 utilizing LLMs for code generation or analysis
- Technical leads integrating AI tools into their workflows
- Software professionals exploring LLM integrations
- Practical experience in software development or scripting
- Familiarity with widely used programming languages (e.g., Python, JavaScript, SQL)
- Fundamental 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
