Over the last few days, I built and launched a small SaaS called Book-Writer-AI — a tool that generates full books using AI, chapter by chapter, with controllable tone, pacing, characters and structure.
It’s available here: https://book-writer-ai.com
(Some books generated by users are already public and readable on the site — a surprisingly fun bonus feature.)
This is the story of how I built it fast using
PHP, vanilla SQL, Bootstrap, Redis, Claude + OpenAI APIs, and Stripe, and the technical challenges that came with generating long-form narratives using LLMs.
🚀 Tech Stack
Because I wanted to ship fast, I used a very lean and predictable stack:
Backend: PHP (vanilla, no framework — to keep it fast & simple)
Database: MySQL with manually designed SQL tables
Cache / Queue: Redis
_Frontend: _Bootstrap
AI Models: Claude 3.5 Sonnet + OpenAI GPT-4.1 for fallback
Payments: Stripe
_Hosting: _A basic Ubuntu VPS
I built almost everything in a few days — which forced me to focus only on what mattered for an MVP:** structure, coherence, and predictable generation.**
🧠 The Challenge: LLMs Are Bad at Writing Long Books
One of the first issues with using AI to write books is something every developer who works with LLMs knows:
LLMs have short memories.
Even with large context windows (200k+), long-form consistency is still a problem:
Characters change personality mid-story
Plot threads get forgotten
Style and tone drift
Previously generated sections become irrelevant
“Context stuffing” becomes expensive and slow
Trying to generate a full 30k–50k-word book in a single long prompt is simply impossible — or at least unreliable.
So I needed a system capable of generating small, coherent pieces while keeping them connected to a global narrative structure.
📚 Solution: A Multi-Layered Story Architecture
To deal with LLM limitations, I built the backend around two key SQL structures.
1. Overall Plot Structure Table
plot_structure contains the entire macro-structure of the book: acts, arcs, turning points, midpoint, climax, resolution, etc.
The idea:
→ The model should always know where we are in the story.
plot_structure (
act_1_percentage,
act_1_description,
act_1_key_events,
act_2_description,
midpoint,
climax,
resolution
)
When generating chapters, I feed the relevant slice of this structure — not the whole book — keeping the prompt short, cheap, and focused.
This prevents the “chapter 7 has nothing to do with chapter 3” syndrome.
2. Fine-Grained Chapter Part Table
Instead of generating an entire chapter at once, I split chapters into smaller parts, each with targeted metadata.
This is stored in the chapter_parts table.
Each part includes:
- POV
- Characters involved
- Setting
- Atmosphere
- Key events
- Word-count targets
- Ratios for tone, tension, dialogue, pace
- Writing instructions
- And finally: the generated content
This lets me ask the LLM to focus on a 300–500 word micro-scene with very specific goals instead of a massive 2k–4k word chapter.
🎚️ Tone, Pacing & Style via Ratio-Based Controls
One of the features I added is a lightweight “ratio-based tone system”.
Every chapter part contains numeric weights like:
- tension
- descriptive_tone
- character_development
- action_level
- emotional_intensity
- dialogue_ratio
- pacing
All values are normalized on a 0–1 scale (I used a “1-based ratio” design for rapid prototyping).
These values are injected into the prompt like:
“Increase dialogue to 0.70, reduce descriptive tone to 0.30, maintain tension at 0.55.”
This gives the LLM guidance without micromanagement, resulting in more consistent stylistic identity across the book.
⚡ Redis for Speed & Retry Logic
Since generation is slow, expensive, and sometimes fails, Redis handles:
Job queues
Status (pending, writing, finished, failed)
Retry logic
Caching previously generated plot elements
This keeps the PHP backend extremely lean.
👀 Making Books Publicly Readable (A Surprisingly Good Feature)
I wasn’t planning it originally, but I added the ability for users to make their generated books publicly readable and shareable.
This turned out to be:
A discovery feature
A social proof feature
A traffic generator
A retention loop (users return to see each other’s books)
I’ve already seen users browse other AI-generated books just out of curiosity.
⏱️ Built in a Few Days
The entire system — plot generator, chapter generator, database schema, UI, payment logic — was built in a few days.
It is absolutely not perfect.
But it works.
It generates readable multi-chapter stories with decent consistency.
And most importantly:
It ships.
🧪 What I Learned About AI Book Generation
LLMs need structure, not freedom
Long-context models still drift, even with 200k tokens
Breaking everything into small parts is essential
Coherence is an architectural problem, not just a prompting problem
SQL is a great “memory extension mechanism” for LLMs
Tone ratios give more control than trying to force “write like X” prompts while i did usze that too
📬 If You Want to Try It
You can check it out here:
https://book-writer-ai.com
Some books are already publicly readable — feel free to explore or generate your own.
❓ Question to the Dev.to Community
For those of you with experience in SaaS or indie projects:
What’s the best way to promote something like this effectively?
Long-form content?
Reddit?
YouTube?
Partnerships?
SEO?
Or a completely different approach?
I’d love your honest thoughts.