Course Objective & Scope
Ad-hoc chat prompts may work for quick personal tasks, but enterprise processes demand predictable, auditable, and repeatable outputs. Moving to production quality requires shifting from casual prompting to systematic context engineering and multi-step workflow design. This 1-2 day hands-on lab teaches participants how to decompose complex business tasks, supply the exact context models need, and enforce structured outputs.
Throughout the lab, participants work on real scenarios from their organization to build a complete prompt and workflow package—including parameterized system instructions, few-shot example libraries, domain context packs, and evaluation rubrics. Every workflow is stress-tested against edge cases and ambiguous inputs before wrap-up.
Who Should Attend?
- Product managers and business analysts designing standardized AI templates and assistant behaviors
- Operations, content, and knowledge management specialists building LLM-powered internal workflows
- Internal AI champions, process designers, and solution architects
- Team leads looking to establish reusable, reliable AI workflows across their departments
Key Takeaways
- Refactor fragile monolithic prompts into modular, parameterized system and user prompt templates
- Assemble high-signal context packs combining domain rules, reference snippets, glossary terms, and negative constraints
- Decompose complex tasks into multi-step workflows using prompt chaining, classification routing, and verification steps
- Enforce deterministic output schemas (JSON, structured tables) and implement evaluator/self-critique loops
- Test and version prompt workflows against a golden dataset using clear quality rubrics
Curriculum
Production-Grade Prompt Architecture
- Moving from ad-hoc chat to parameterized templates: role, task, constraints, and output contracts
- Structured input/output separation using XML/Markdown delimiters and strict JSON schemas
- Common failure modes: conflicting instructions, cognitive overload, and vague success criteria
Context Engineering and Knowledge Packaging
- Curating high-signal context: stripping noise while preserving critical domain rules
- Building reusable context packs: style guides, domain glossaries, edge-case rules, and few-shot examples
- Managing context window budgets and mitigating lost-in-the-middle degradation
Multi-Step LLM Workflow Design
- Task decomposition patterns: Classify -> Extract -> Synthesize -> Verify pipelines
- Prompt chaining, conditional intent routing, and parallel subtask execution
- Designing fallback paths and human-in-the-loop checkpoints for low-confidence or ambiguous inputs
Quality Rubrics and Systematic Evaluation
- Constructing a golden test set of 10-20 representative and adversarial edge cases
- Defining explicit scoring rubrics for factual grounding, completeness, schema compliance, and tone
- Running iterative evaluations using human review and LLM-as-a-Judge prompts
Design Lab: Building an Enterprise Workflow Package
- Hands-on build of an end-to-end prompt, context, and workflow blueprint for a real company process
- Peer stress-testing: red-teaming another group's workflow with tricky edge cases
- Packaging templates with versioning, usage guidelines, and handoff documentation
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