AI TRAINING
Advanced Prompt Engineering for Software Engineers
Master production-grade prompting techniques to build reliable, testable, and scalable LLM-powered applications.
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Run the diagnostic →What it covers
This practitioner-level programme equips engineers with advanced prompting strategies used in production LLM systems, including structured output generation, function calling, chain-of-thought reasoning, and few-shot pattern design. Participants learn to build eval-driven iteration loops to measure and improve prompt performance systematically. The format combines hands-on coding labs with real-world case studies, covering both OpenAI and open-source model APIs. By the end, engineers can design, test, and maintain robust prompt pipelines ready for production deployment.
What you'll be able to do
- Design and validate structured-output prompts that reliably return well-formed JSON conforming to a defined schema
- Implement function-calling pipelines in which an LLM correctly selects and parameterises tools across multi-turn conversations
- Apply chain-of-thought and self-consistency techniques to measurably improve model accuracy on reasoning tasks
- Build an automated prompt evaluation suite with quantitative metrics and integrate it into a CI pipeline
- Version and regression-test prompt templates so that model upgrades do not silently degrade production behaviour
Topics covered
- Structured output generation: JSON mode, grammar-constrained decoding, and schema validation
- Function calling and tool use: designing reliable tool-calling agents
- Chain-of-thought and reasoning prompts: zero-shot CoT, self-consistency, tree-of-thought
- Few-shot and many-shot prompting: pattern selection, example ordering, and diversity
- Eval-driven prompt iteration: building automated test suites for prompt quality
- System prompt architecture: role separation, context management, and injection defence
- Retrieval-augmented generation (RAG) prompt integration and grounding strategies
- Prompt versioning, regression testing, and CI/CD integration for LLM pipelines
Delivery
Delivered as a 2-3 day intensive bootcamp, available in-person or fully remote via video conferencing with shared coding environments (e.g., GitHub Codespaces or JupyterHub). Approximately 70% of time is hands-on lab work; 30% is concept delivery and code review. Participants work on a capstone prompt pipeline project throughout. Materials include a private GitHub repo with starter notebooks, evaluation harness templates, and a reference prompt library. A follow-up 90-minute Q&A session is included two weeks after the bootcamp.
What makes it work
- Establishing an eval harness with clear metrics before starting prompt iteration, not after
- Treating prompt engineering as a collaborative discipline between product, data, and engineering teams
- Running prompt regression tests in CI so every model or prompt change is automatically validated
- Starting with the simplest effective prompt and adding complexity only when evals demonstrate a need
Common mistakes
- Treating prompts as static strings rather than versioned, testable artefacts managed in source control
- Relying on manual human review instead of automated evals, making prompt iteration slow and subjective
- Ignoring model-specific behaviours and assuming prompts transfer perfectly across different LLMs or model versions
- Over-engineering complex chained prompts before validating that simpler approaches fail on the actual task
When NOT to take this
This training is not the right fit for a team that has not yet shipped any LLM feature and is still evaluating whether to use AI at all — start with a literacy or awareness workshop first to align on use cases before investing in advanced prompting techniques.
Providers to consider
Sources
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Other trainings at this level
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.