Building LLM-Based Applications with Azure OpenAI with Jay Emery - #657

Generative AI and large language models are transforming how startups and digital-native organizations build products, shifting from traditional enterprise partnerships to AI-centric integrations. Security and data privacy remain primary concerns for leadership, requiring clear verification that proprietary data is not used for model training. Technical implementation strategies focus on three pillars: prompt engineering, fine-tuning, and retrieval-augmented generation (RAG). Prompt chaining and variance tools help optimize outputs, while RAG allows for context-specific responses without the high costs of fine-tuning. Performance management involves balancing model selection—such as choosing between GPT-3.5 Turbo and GPT-4—and utilizing provisioned throughput units to ensure consistent latency. Effective cost management relies on token-level monitoring and pre-processing heuristics to route workloads efficiently, ensuring that organizations maximize value from their AI investments while maintaining operational scalability.