Overview

This comprehensive course is for product managers, ML engineers, and technical leads responsible for transforming LLM concepts into reliable, cost-effective production services. In today's AI-driven landscape, building a functional model is only the beginning. You will learn the complete framework for measuring, documenting, and optimizing LLM applications to ensure that they deliver real business value efficiently and consistently. The course begins by grounding you in product-centric development, teaching you to create a clear Product Requirements Document (PRD) that defines scope, MVP features, and success metrics. You'll evaluate features against acceptance criteria to identify gaps and validate user requirements. You will evaluate Zero-Shot, Few-Shot, and Chain-of-Thought prompt patterns and develop runbooks for vector index management. You will learn to analyze compute-spend reports to propose concrete cost-reduction strategies, such as model quantization, and use value-stream mapping to identify and eliminate inefficiencies in your development and release pipelines.

Syllabus

Develop and Evaluate LLM Features Effectively

This module teaches how to prevent LLM failures—like "hallucinated" advice—through professional product management. You will learn to draft a Product Requirements Document (PRD) as a single source of truth for scope, MVP features, and success metrics. The curriculum transitions from planning to validation, covering User Acceptance Testing (UAT) based on testable user stories. Through hands-on activities, you’ll draft a PRD for an HR chatbot and test for dangerous edge cases. By the end, you’ll be equipped to deliver safe, effective AI features that align with your business vision.

Document and Evaluate LLM Prompting Success

This module provides ML engineers and practitioners with the operational discipline needed to transition LLM prototypes into reliable production services. You will move from "prompt artistry" to prompt science, learning to systematically evaluate and A/B test prompt patterns while balancing response quality, consistency, and token costs. The curriculum focuses on creating professional-grade operational documentation, such as step-by-step run-books for vector index updates, complete with validation checks and rollback procedures. By developing an LLMOps Production-Readiness Toolkit, you will gain the expertise to make data-driven decisions that ensure both high performance and cost efficiency in live AI systems.

Optimize LLM Costs and Streamline Processes

This module bridges technical execution and operational excellence for ML practitioners. You will master two critical pillars: cost optimization and process streamlining. First, you’ll dive into MLOps financials, learning to dissect compute-spend reports and implement technical optimizations like INT8 quantization to reduce overhead. Next, you will apply Value-Stream Mapping (VSM) to ML pipelines using tools like Miro to visualize workflows and eliminate manual bottlenecks. By the end, you’ll be equipped to design automated, future-state processes that ensure your LLM deployments are fast, cost-efficient, and business-aligned.

Conducting a 360-Degree Audit of an LLM-Powered Chatbot

Step into the role of a senior analyst tasked with overhauling an underperforming and costly LLM chatbot. In this module, you will conduct a comprehensive 360-degree audit to diagnose core issues across product, performance, and process. You’ll define KPIs, perform a feature gap-analysis, run experiments to optimize prompt strategies, and use value-stream mapping and cost modeling to identify savings and efficiencies, delivering actionable recommendations to improve performance, reduce costs, and create a high-value asset for your portfolio.