Large Language Models (LLMs) such as GPT-4o, Claude, and Gemini can synthesize text, code, and knowledge at human-like quality. Prompt engineering—the craft of structuring inputs to guide an LLM—unlocks that power for day-to-day software projects. This masterclass shows how to blend LLM APIs with familiar cloud patterns (microservices, event streams, caches) so your apps become smarter while staying reliable, secure, and cost-efficient

• LLM Fundamentals – tokens, context windows, temperature, and why model “hallucinations” happen.
• Prompt Engineering Patterns – system vs. user prompts, few-shot examples, chain-of-thought, and function calling.
• Scalable Integration – caching frequent answers, batching requests, back-pressure handling, and fallbacks to deterministic logic.
• Retrieval-Augmented Generation – pairing LLMs with vector databases (Pinecone, Milvus) to ground answers in real company data.
• Observability & Guardrails – logging prompts/responses, detecting PII leaks, rate-limiting, and automatic safety checks.