Prompt Engineering Masterclass: From Good Prompts to Reliable Workflows

​Learn how to turn vague asks into structured, reliable outputs with modern prompt engineering. In this hands-on masterclass, you’ll learn how to break down tasks, frame effective roles and context, set clear constraints, elicit useful reasoning, and design lightweight “agentic” patterns such as tool calls, retrieval, and review steps.

​Prompting has evolved from clever wording into repeatable systems design: structured outputs (JSON Schema), function and tool calling, context engineering for RAG, and built-in verification. With today’s reasoning-class models, simple, explicit instructions often outperform sprawling meta-prompts—and true reliability now depends on clear output schemas, deliberate tool use, and post-generation checks.

​By the end of this workshop, you’ll be able to:

1. ​Design prompts as systems

   • ​Use a prompt specification (objective → inputs → constraints → style → schema → tests) and instruction hierarchy (system developer user) to remove ambiguity and drift. You’ll also learn when to keep prompts short and direct for reasoning-class models.

2. ​Guarantee formats with structured outputs

   • ​Drive exact JSON Schema responses (dates, enums, arrays) to stop flaky parsing and enable downstream automation. We’ll cover schema design tips, enums vs. anyOf, and graceful failure handling.

3. ​Call tools safely with function calling

   • ​Bind models to your functions/APIs and design deterministic tool-use prompts (names, arguments, preconditions), including safe parallel tool-use patterns and guardrails.

4. ​Engineer context (RAG) that the model can actually use

   • ​Apply chunking, re-ranking, and instructional context that reduces hallucinations. You’ll use “cite-and-verify” templates and learn when not to overfill the context window.

5. ​Use multimodal prompting (text × image × audio/video)

   • ​Attach images and documents with precise, reference-style instructions (for example: “In Figure A, list anomalies ≥ 3%”). Learn modality addressing, grounding, and verbosity control.

6. ​Build verification loops that improve trust

   • ​Implement patterns such as structured self-checklists, retrieval-based citation checks, and self-consistency sampling when accuracy matters most—and understand when critique loops can hurt performance.

7. ​Write security and safety prompts

   • ​Design prompt-injection–resistant patterns, use least-privilege tools, and layer content-safety shields aligned with current best practices (e.g., OWASP LLM Top 10–style thinking).

8. ​Tune cost and latency with prompt caching

   • ​Structure prompts for cache hits, reuse long static headers and examples across calls, and understand provider-specific knobs for performance and cost control.

​You’ll leave with:

​– A production-ready prompt kit (templates + checklists) you can drop into your workflow.

​– A “Prompt QA” checklist for accuracy, tone, safety, and bias.

​– Three mini-workflows—write → review → revise, analyze → summarize → decide, and plan → act → report—that you can adapt immediately to your own work or personal projects.

​Prerequisites & setup:

​– An account for at least one of: OpenAI, Anthropic, or Google AI Studio / Gemini (free or paid is fine).

​– A modern browser; optionally Postman or cURL for API demos.

​– We provide a small sample dataset (PDF + CSV) and a mock API endpoint for exercises.

​Instructor:

​Andrew Tsintsiruk (Founder & CEO, Rohic Inc. / Mentor at The Upskilling Labs) — builds collaborative AI agents for go-to-market teams, with a focus on hierarchical orchestration, tool calling, RAG, and human-in-the-loop controls.

​Format & logistics:

​– Format: Live, hands-on workshop.

​– Duration: 1.5–2 hours.

​– Recording & materials: Slides, lab notebooks, and all templates are included.

​– Capacity: 30–60 participants.