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Course Outline
Best Practices and Implementation Tools
Identification of Common Pitfalls and Corresponding Mitigation Strategies
Fundamentals of Prompt Engineering for Federal Applications
Iterative Refinement and Design of Prompts
Application of Prompting in Test Automation and Structured Query Language (SQL) Generation
Summary and Direction for Future Initiatives
Utilization of Prompts for Code Explanation and Diagnostic Support
Development of Prompts for Automated Code Generation
- Prevention of hallucinated code outputs and mitigation of security vulnerabilities
- Management of incomplete or ambiguous input requirements
- Establishment of secure fallback mechanisms and operational guardrails
- Derivation of test cases from functional requirements or existing codebases
- Conversion of natural language descriptions into structured SQL queries
- Standardization of output formats for seamless integration into automated test suites
- Clarification of legacy or previously undocumented code systems
- Facilitation of logical walkthroughs and edge case analysis via targeted prompts
- Identification and documentation of defects or performance inefficiencies
- Generation of code artifacts from plain-language specifications
- Specification of output formatting conventions and target programming languages
- Execution of complex logic processing across multiple function dependencies
- Optimization of results through prompt chaining and continuous feedback mechanisms
- Implementation of error recovery protocols and prompt tuning strategies
- Analysis of case studies demonstrating refinement techniques for technical operations
- Development of reusable prompt libraries and established usage patterns
- Deployment of prompt templates within integrated development environments (IDEs) such as VS Code or API-driven workflows
- Assessment of prompt efficacy and performance metrics in production environments
- Comprehension of core components: prompts, context windows, token limits, and model behaviors
- Classification of prompt methodologies: zero-shot, one-shot, and few-shot learning
- Distinction between system-level directives and user-provided instructions across various application programming interfaces (APIs)
Requirements
Target Audience
- Software engineers leveraging large language models for code creation and evaluation
- Technical management personnel assessing artificial intelligence solutions for integration into standard operating procedures
- IT professionals investigating implementations of generative AI within enterprise environments for government
- Demonstrated proficiency in software development or scripting methodologies
- Working knowledge of prevalent programming languages, including Python, JavaScript, and SQL
- Foundational comprehension of large language models and associated AI technologies 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