Prompt Engineering & LLM Mastery

Who this is for

You use ChatGPT, Claude, Gemini, or other LLMs but you feel like you are barely scratching the surface. Your prompts produce generic, unreliable, or shallow outputs. You have heard about agents, RAG, function calling, and system prompts but you are not sure how to put it all together. This session is for professionals, developers, entrepreneurs, and researchers who want to go from casual AI user to someone who genuinely commands these tools at a high level.

What we cover

• The principles that actually drive output quality: why model family matters for prompt structure, and where prompt engineering ends and model-appropriate instruction design begins
• When chain-of-thought reasoning helps and when it actively hurts: CoT improves multi-step reasoning but degrades constrained-format outputs like JSON generation or classification, where it introduces hallucination surface area
• System prompt architecture: role anchoring, behavioral constraints, output format enforcement, and knowledge injection, and how to stack these without conflicting instructions degrading each other
• Prompt patterns across every major LLM - zero-shot, few-shot, chain-of-thought, and beyond
• Building multi-step prompt chains for complex workflows
• When agents are the right tool and when they are not: the reliability cost of autonomous tool-calling, why deterministic chains outperform agents for most business workflows, and where LangGraph or the OpenAI Agents SDK earns its complexity
• RAG explained at the level that matters for practitioners: chunk size and overlap decisions, embedding model selection, hybrid search, reranking, and why retrieval quality - not generation quality - is usually what breaks a RAG product
• Structured output reliability: enforcing JSON schema compliance through function calling versus relying on instruction-following, and how each breaks under edge cases
• Hands-on application to your specific use case during the session

What you walk away with