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Duration 7 hours
Course Outline
Optimal Practices and Tooling
Typical Errors and Countermeasures
Foundations of Prompt Engineering
Refining Prompts and Iterative Design
Prompts for Test Automation and SQL Creation
Recap and Future Directions
Utilizing Prompts for Code Explanation and Debugging
Crafting Prompts for Code Generation
- Preventing hallucinated code and security risks
- Managing incomplete or vague inputs
- Developing safe fallback prompts and guardrails
- Deriving test cases from requirements or existing code
- Creating structured SQL queries from natural language
- Structuring outputs for integration into test suites
- Interpreting legacy or unfamiliar code
- Requesting logic breakdowns or edge case analysis
- Identifying and explaining bugs or inefficiencies
- Generating code from simple descriptions
- Managing output format and target programming language
- Addressing complex logic or multi-function scenarios
- Enhancing outcomes via prompt chaining and feedback cycles
- Error recovery and prompt tuning methods
- Case studies on refining technical tasks
- Prompt libraries and reuse patterns
- Implementing prompt templates in VS Code or API-based workflows
- Assessing prompt quality and performance in production
- Grasping prompts, context, tokens, and model mechanics
- Prompt types: zero-shot, one-shot, few-shot
- Applying system vs. user instructions across various APIs
Requirements
Target Audience
- Developers utilizing LLMs for code generation or analysis
- Technical leads evaluating AI tools within their workflows
- Software professionals testing LLM integrations
- Background 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
