May 13
Loading video player...
A strong LLM product is not just a clever prompt—it is a complete system. In this lesson, we connect context, retrieval, model behavior, evaluation, guardrails, and deployment into one practical design for a real support assistant. You’ll see how product teams can move from theory to implementation by designing AI features that are accurate, safe, measurable, and ready for real users. What you’ll learn: - How to define product behavior before choosing RAG, fine-tuning, or a model - Why context should be treated as a pipeline, not a single prompt - How trusted docs, account data, user intent, and session history work together - When to use prompting, RAG, fine-tuning, rules, or classical ML - How retrieval design affects accuracy, latency, citations, and user trust - Why evaluation, feedback loops, and escalation rules matter in production Course progression: this module builds on earlier concepts such as LLM context, RAG fundamentals, fine-tuning behavior, and model selection. It then moves into practical feature architecture using a support assistant use case, helping learners understand how real AI product decisions are made. For corporate training programs on Generative AI, LLM application design, RAG systems, and AI product strategy, contact KryptoMindz. Visit: https://kryptomindz.com Email: mustafa@kryptomindz.com Phone: +91-9873062228 #LLM #RAG #GenerativeAI #AIProductManagement #FineTuning #AIEvaluation #CorporateTraining #KryptoMindz