Prompt to Production — The Complete Field Guide to Large Language Models (and the Systems Around Them)

Everyone talks about large language models. Very few actually understand the machine.

Prompt to Production is the field guide that takes you from "I know software" to "I can follow, question, and contribute in any room where LLMs are discussed." It follows a single, connected story — one model, traced from raw text all the way to a governed production system — and, just as deliberately, the systems built around it that most introductions skip: retrieval, agents, knowledge graphs, ontologies with small models, guardrails, and operations.

No hand-waving, no hype, and no wall of math. Every idea is explained in plain language first, with the formula or code as optional depth — and reinforced with original diagrams, mind maps, flowcharts, flashcards, and quizzes so it actually sticks.

What you'll master

- 🧠 The Transformer and self-attention — the one equation behind modern AI, explained intuitively

- 🏗️ The three-stage training pipeline: pretraining, fine-tuning (LoRA), and alignment (RLHF / DPO)

- 🗺️ Reading the model landscape: sizes, context windows, frontier vs open, and multimodal models

- 💬 Prompt engineering, RAG, AI agents, and the modern tooling ecosystem (Hugging Face, LangChain, LangGraph)

- 🕸️ Knowledge graphs, ontologies with small models, and the neuro-symbolic patterns beneath them

- 🚀 Shipping to production — serving, LLMOps, cost, and the risks, guardrails, and governance that keep it safe

- 🔑 The two master algorithms underneath it all: backpropagation and recursion

Inside the book

- Full-colour PDF (116 pages) + reflowable EPUB for Kindle/Apple Books

- 16 chapters: Transformers → Training → Prompt Engineering → RAG → Agents → Knowledge Graphs → Production → Governance

- 105 flashcards with spaced-repetition schedule

- Chapter quizzes + 25-question master quiz

- 5 part mind maps + 40+ original diagrams

- One-page "whole book on a page" structured report

- Full glossary and curated references

Who it's for

Students and early-career engineers · software and development teams · data and analytics professionals · product and program managers · tech managers and leaders · and curious professionals who want a jargon-free but genuine understanding.

A software background deepens the experience, but the core ideas are built from the ground up — no prior AI coursework required.

Researched, architected, and composed by Rajkamal Mishra. Developed with AI assistance under human direction; every concept selected, verified, and reviewed by the author. Original work — checked against a similarity review.

"Whoever you are in the room — the one being asked, or the one deciding — this book puts you on the side of the conversation that understands the machinery."