Prompt Engineering for Software Development

Audience

• Developers using LLMs in code generation or analysis
• Technical leads exploring AI tools in workflows
• Software professionals experimenting with LLM integrations

• Experience in software development or scripting
• Familiarity with common programming languages (e.g., Python, JavaScript, SQL)
• Basic understanding of large language models and AI tools like ChatGPT, Claude, or Copilot

Course Customization Options

Format of the Course

By the end of this training, participants will be able to:

Prompt Engineering for Software Development is a hands-on course designed to teach developers how to craft effective prompts that optimize the usefulness, precision, and safety of AI-generated outputs in the context of software engineering tasks.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to integrate prompt engineering techniques into their use of AI coding assistants, code analyzers, and intelligent automation tools.

• Create precise prompts for code generation, review, and optimization tasks.
• Use prompt chaining and refinement to improve AI responses in iterative workflows.
• Generate structured outputs such as test cases, SQL queries, and documentation from minimal input.
• Mitigate hallucination and improve control over LLM behavior in technical environments.

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

• To request a customized training for this course, please contact us to arrange.

Best Practices and Tools

Common Pitfalls and Mitigation Strategies

Introduction to Prompt Engineering

Prompt Refinement and Iterative Design

Prompting for Test Automation and SQL Generation

Summary and Next Steps

Using Prompts for Code Explanation and Debugging

Writing Prompts for Code Generation

• Avoiding hallucinated code or security vulnerabilities
• Handling incomplete or ambiguous inputs
• Creating safe fallback prompts and guardrails

• Creating test cases from requirements or code
• Generating structured SQL queries from natural language
• Formatting outputs for integration into test suites

• Explaining legacy or unfamiliar code
• Prompting for logic walkthroughs or edge case analysis
• Finding and explaining bugs or inefficiencies

• Generating code from plain-language descriptions
• Controlling output format and programming language
• Working with complex logic or multiple functions

• Improving results through prompt chaining and feedback loops
• Error recovery and prompt tuning strategies
• Case studies in refinement for technical tasks

• Prompt libraries and reuse patterns
• Using prompt templates in VS Code or API-based workflows
• Evaluating prompt quality and performance in production use

• Understanding prompts, context, tokens, and models
• Prompt types: zero-shot, one-shot, few-shot
• Using system vs. user instructions in different APIs