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Duration 7 hours
Course Outline
Industry Best Practices and Tooling
Addressing Common Pitfalls and Mitigation Tactics
Foundamentals of Prompt Engineering
Iterative Design and Prompt Refinement
Prompting Strategies for Test Automation and SQL Creation
Conclusion and Recommended Next Steps
Leveraging Prompts for Code Analysis and Debugging
Crafting Prompts for Code Generation
- Preventing hallucinated code and security loopholes.
- Managing incomplete or ambiguous user inputs.
- Establishing safe fallback prompts and protective guardrails.
- Deriving test cases from requirements or existing code.
- Translating natural language into structured SQL queries.
- Structuring outputs for seamless integration into test suites.
- Interpreting legacy or complex codebases.
- Requesting logic breakdowns and edge case evaluations.
- Identifying and clarifying bugs or performance bottlenecks.
- Generating code from descriptive plain language.
- Directing output formats and selecting specific programming languages.
- Managing complex logic and multi-function implementations.
- Enhancing outcomes via prompt chaining and feedback mechanisms.
- Strategies for error recovery and prompt optimization.
- Real-world case studies on refining prompts for technical tasks.
- Utilizing prompt libraries and reusable patterns.
- Implementing prompt templates in VS Code or API-driven workflows.
- Assessing prompt efficacy and performance in production environments.
- Grasping core concepts: prompts, context, tokens, and model architectures.
- Differentiating prompt types: zero-shot, one-shot, and few-shot.
- Applying system vs. user instructions across various API interfaces.
Requirements
Target Audience
- Developers utilizing LLMs for code generation or analysis.
- Technical leads evaluating AI tools for their team workflows.
- Software professionals exploring the integration of LLMs into their projects.
- Background in software development or scripting.
- Proficiency in standard programming languages such as Python, JavaScript, or SQL.
- Foundational knowledge of large language models and AI platforms like 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
