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Duration 7 hours
Course Outline
Optimal Practices and Essential Tools
Addressing Common Errors and Mitigation Tactics
Fundamentals of Prompt Engineering
Refining Prompts and Iterative Design Strategies
Prompting for Test Automation and SQL Generation
Recap and Future Directions
Utilising Prompts for Code Interpretation and Debugging
Crafting Prompts for Code Creation
- Preventing the generation of hallucinated code or security flaws
- Managing incomplete or ambiguous user inputs
- Establishing secure fallback prompts and operational 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
- Generating prompts for logic walkthroughs and edge case evaluation
- Identifying and explaining bugs or performance inefficiencies
- Producing code from descriptive plain-language instructions
- Regulating output structure and target programming language
- Handling complex logic or multi-function implementations
- Enhancing outcomes via prompt chaining and feedback mechanisms
- Strategies for error recovery and prompt optimisation
- Case studies focused on refining prompts for technical tasks
- Prompt libraries and reusable patterns
- Implementing prompt templates within VS Code or API-centric workflows
- Assessing prompt efficacy and performance in production environments
- Grasping the relationship between prompts, context, tokens, and models
- Differentiating prompt types: zero-shot, one-shot, and few-shot
- Utilising system versus user instructions across various APIs
Requirements
Target Audience
- Developers leveraging LLMs for code generation or analysis
- Technical leaders investigating AI tools within their workflows
- Software specialists exploring LLM integrations
- Practical experience in software development or scripting
- Proficiency in standard programming languages such as Python, JavaScript, and 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
