Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Best Practices and Tooling
Common Pitfalls and Mitigation Techniques
Foundations of Prompt Engineering
Prompt Refinement and Iterative Design
Prompting for Test Automation and SQL Generation
Conclusion and Future Steps
Applying Prompts to Code Explanation and Debugging
Crafting Prompts for Code Generation
- Preventing hallucinated code or security vulnerabilities.
- Managing incomplete or ambiguous input data.
- Designing safe fallback prompts and guardrails.
- Deriving test cases from requirements or existing code.
- Generating structured SQL queries from natural language descriptions.
- Formatting outputs for seamless integration into test suites.
- Interpreting legacy or unfamiliar code segments.
- Requesting logic walkthroughs or edge-case analysis.
- Identifying and explaining bugs or performance inefficiencies.
- Generating code from plain-language descriptions.
- Specifying output formats and programming languages.
- Handling complex logic or multi-function tasks.
- Enhancing outcomes through prompt chaining and feedback loops.
- Implementing error recovery and prompt tuning strategies.
- Examining case studies on refinement for technical tasks.
- Developing prompt libraries and reuse patterns.
- Utilising prompt templates within VS Code or API-based workflows.
- Assessing prompt quality and performance in production environments.
- Grasping the concepts of prompts, context, tokens, and models.
- Distinguishing between zero-shot, one-shot, and few-shot prompting.
- Applying system versus user instructions across various APIs.
Requirements
Target Audience
- Developers leveraging LLMs for code generation or analysis.
- Technical leads investigating the integration of AI tools into their workflows.
- Software professionals exploring LLM integrations.
- Practical experience in software development or scripting.
- Proficiency with standard programming languages (e.g., Python, JavaScript, SQL).
- Foundational knowledge of large language models and AI utilities such as ChatGPT, Claude, or Copilot.
7 Hours
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
