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Duration 7 hours
Course Outline
Best Practices and Toolkits
Addressing Common Challenges and Mitigation Strategies
Fundamentals of Prompt Engineering
Iterative Design and Prompt Refinement
Prompting for Test Automation and SQL Creation
Wrap-up and Future Directions
Leveraging Prompts for Code Explanation and Debugging
Crafting Prompts for Code Generation
- Preventing the generation of erroneous code or security flaws.
- Managing ambiguous or incomplete input data.
- Establishing safe fallback prompts and safety guardrails.
- Deriving test cases from requirements or existing code.
- Converting natural language into structured SQL queries.
- Formatting outputs for seamless integration into test suites.
- Interpreting legacy or unfamiliar codebases.
- Requesting logic walkthroughs or edge-case analyses.
- Identifying and explaining bugs or performance bottlenecks.
- Generating code from plain-text descriptions.
- Specifying output formats and target programming languages.
- Handling complex logic or multi-function requirements.
- Enhancing outcomes via prompt chaining and feedback loops.
- Strategies for error recovery and prompt tuning.
- Refinement case studies focused on technical tasks.
- Utilizing prompt libraries and reuse patterns.
- Implementing prompt templates in VS Code or API-driven workflows.
- Assessing prompt quality and performance in production environments.
- Grasping key concepts: prompts, context, tokens, and models.
- Understanding prompt types: zero-shot, one-shot, and few-shot.
- Distinguishing between system and user instructions across different APIs.
Requirements
Target Audience
- Developers leveraging LLMs for code generation or analysis.
- Technical leads investigating AI tools within their workflows.
- Software professionals exploring LLM integrations.
- Solid experience in software development or scripting.
- Knowledge of standard programming languages (e.g., Python, JavaScript, SQL).
- A 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
