Module 27: LLM Strategy, Product Management & ROI

Chapter Overview

Technical excellence alone does not guarantee successful LLM adoption. Organizations that thrive with AI combine strong engineering foundations with disciplined strategy, clear product thinking, and rigorous return-on-investment measurement. Without these business-facing capabilities, even the most sophisticated models end up as expensive prototypes that never reach production.

This module bridges the gap between LLM engineering and business impact. It begins with strategic frameworks for identifying and prioritizing AI use cases, then covers the product management skills needed to translate business problems into LLM-powered solutions. The module provides concrete ROI measurement techniques for quantifying value, vendor evaluation frameworks for make-or-buy decisions, and infrastructure planning guidance for compute budgeting and deployment architecture.

Whether you are an individual contributor building the business case for an LLM project, a tech lead evaluating vendors, or a product manager defining success metrics, this module equips you with the frameworks and vocabulary to connect technical decisions to organizational outcomes.

Learning Objectives

Conduct AI readiness assessments and build structured use-case prioritization matrices
Translate business problems into LLM product requirements with measurable success criteria
Define and track LLM product metrics including CSAT, resolution rate, deflection rate, and hallucination rate
Build ROI models for common LLM use cases (coding assistants, customer support, content generation)
Evaluate LLM providers, vector databases, and agent frameworks using structured scoring rubrics
Apply build-versus-buy decision trees that account for total cost of ownership over 12 to 36 months
Plan compute budgets for training and inference workloads across GPU tiers (A100, H100, L40S)
Design multi-cloud inference architectures with fallback routing and cost optimization

Prerequisites

Module 09: LLM APIs (chat completions, model parameters, structured outputs)
Module 19: Retrieval-Augmented Generation (embedding search, vector stores, RAG pipelines)
Module 21: AI Agents (agent architectures, tool use, planning)
Module 25: Evaluation and Observability (metrics, experiment design, monitoring)
Module 26: Production, Safety, and Ethics (deployment, guardrails, governance)

Chapter Overview

Learning Objectives

Prerequisites

Sections