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Building GenAI Applications: From Prototype to Production

2Architecture & Design

Playbook Overview

This playbook provides a comprehensive, battle-tested framework for building production-ready generative AI applications. It distills practical patterns, architectural blueprints, and real-world lessons from successful GenAI products into actionable guidance for teams shipping LLM-powered systems at scale. From product strategy and unit economics to deployment and observability, this guide covers the complete lifecycle of GenAI application development with an emphasis on reliability, cost control, and defensible moats.

Who This Is For

  • CTOs and Tech Leads architecting GenAI products and evaluating build vs buy decisions
  • ML Engineers and AI Engineers transitioning from prototypes to production LLM applications
  • Product Managers defining GenAI product strategy, wedges, and go-to-market approaches
  • Platform Engineers building infrastructure and tooling for GenAI workloads
  • Engineering Managers establishing processes, evaluation frameworks, and operational excellence for AI teams

What You Will Learn

By the end of this playbook you will have:

  1. Strategic product thinking: Learn to choose defensible moats (data, distribution, trust), evaluate product wedges, run disciplined AI experiments, and establish unit economics before scaling—avoiding the trap of "impressive demos, no business model."
  2. Production architecture patterns: Master the GenAI stack from orchestration and guardrails to model selection (Prompt vs RAG vs Fine-tune), agent patterns, and performance optimization—with practical decision frameworks for each layer.
  3. Prompt engineering as reliability engineering: Implement production-grade prompting with defensive techniques against injection attacks, structured evaluation harnesses, versioning workflows, and CI/CD for prompts as code.
  4. Data-centric AI fundamentals: Build proprietary data flywheels through dataset engineering, synthetic data strategies, and the escalation ladder from prompting to RAG to fine-tuning that creates lasting competitive advantage.
  5. Operational excellence: Deploy robust RAG pipelines, implement comprehensive evaluation frameworks (offline + online + LLM-as-judge), establish monitoring and observability, and learn from real-world patterns that separate winning GenAI products from failed experiments.

A Note on This Playbook

This playbook is a sincere attempt to provide a practitioner's blueprint for production GenAI, moving beyond the code to explore the critical decision-making, trade-offs, and challenges involved.

Important Disclaimers:

  • On Authenticity: The methodologies and frameworks shared here are drawn directly from my professional experience.
  • On Collaboration: These posts were created with the assistance of AI for diagram, code and prose generation. The strategic framing, project context, and real-world insights that guide the content are entirely my own.

Chapters

Chapter 1

GenAI Product Planning & Strategy Playbook

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Practical guide to choosing defensive moats, evaluating product wedges, running disciplined AI experiments, and establishing unit economics for GenAI products.

Chapter 2

GenAI Product Architecture

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Production architecture patterns covering orchestration layers, decision flows for Prompt vs RAG vs Fine-tune, guardrails, agents, performance optimization, and evaluation frameworks.

Chapter 3

Prompt Engineering

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Production-grade prompt engineering covering model selection, sampling controls, prompt anatomy, defensive techniques against injection attacks, and lifecycle CI/CD for prompts.

Chapter 4

Data + Models

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Data-centric AI approach covering dataset engineering, data synthesis, model selection framework, and the escalation ladder from prompting to RAG to fine-tuning.

Chapter 5

Fine-Tuning LLMs

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Comprehensive guide to fine-tuning LLMs covering PEFT vs full SFT, LoRA details, alignment strategies (SFT vs RLHF vs DPO), dataset requirements, and production workflow.

Chapter 6

Evaluating Production GenAI Apps

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Comprehensive evaluation framework covering the eval flywheel (design/pre-prod/post-prod phases), evaluation methods, RAG metrics, CI/CD for LLMs, and LLM-as-a-judge techniques.

Chapter 7

Deployment & Serving

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Production LLM infrastructure covering API vs self-hosting decisions, inference fundamentals (TTFT/TPOT), performance engineering, cost modeling, orchestration patterns, and three-horizon migration roadmap.

Chapter 8

RAG - Advanced Strategies

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Advanced RAG techniques covering default architecture, multilingual strategies, multimodal approaches, failure modes and fixes, and minimum viable RAG checklist.

Chapter 9

Inference Optimization

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Comprehensive guide to optimizing LLM inference covering latency (TTFT/TPOT), throughput, cost reduction, KV cache fundamentals, quantization, attention optimizations, and service-level optimization strategies.

Chapter 10

Industry Patterns & Case Studies

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Production patterns from winning GenAI products covering vertical co-pilot architecture, trust stack design, controller-delegate patterns, economics of intelligence, and CTO decision frameworks.

Work With Me

I bring hands-on experience delivering production MLOps and GenAI systems at moderate scale—with minimal infrastructure footprint and cost-effective architectures. I'm excited to collaborate on building next-generation Agentic AI systems. Whether you need expertise in MLOps, GenAI, or Agentic AI—let's connect.

Contact Me