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Duration 7 hours
Course Outline
Best Practices and Essential Tools
Common Pitfalls and Mitigation Strategies
Introduction to Prompt Engineering
Prompt Refinement and Iterative Design
Prompting for Test Automation and SQL Generation
Summary and Future Steps
Utilising Prompts for Code Explanation and Debugging
Crafting Prompts for Code Generation
- Preventing hallucinated code or security vulnerabilities
- Managing incomplete or ambiguous inputs
- Developing safe fallback prompts and guardrails
- Deriving test cases from requirements or existing code
- Generating structured SQL queries from natural language descriptions
- Structuring outputs for seamless integration into test suites
- Explaining legacy or unfamiliar codebases
- Prompting for logic walkthroughs or edge case analysis
- Identifying and explaining bugs or inefficiencies
- Generating code from plain-language specifications
- Controlling output format and target programming language
- Handling complex logic or multi-function requirements
- Enhancing results through prompt chaining and feedback loops
- Error recovery and prompt tuning strategies
- Case studies focusing on refinement for technical tasks
- Prompt libraries and reusable patterns
- Applying prompt templates in VS Code or API-driven workflows
- Assessing prompt quality and performance in production settings
- Grasping the concepts of prompts, context, tokens, and models
- Prompt types: zero-shot, one-shot, and few-shot
- Utilising system versus user instructions across various APIs
Requirements
Target Audience
- Developers utilising LLMs for code generation or analysis
- Technical leads investigating AI tools within their workflows
- Software professionals experimenting with LLM integrations
- Practical experience in software development or scripting
- Proficiency with common programming languages (e.g., Python, JavaScript, SQL)
- A fundamental 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
