Engineering Controllable AI & Getting Those First Users

Following up on my previous post about building AI Story Hub with a filesystem and versioning mindset, I wanted to dive a bit deeper into how we're tackling the "controllable AI" part and share some early insights from getting the first users onboard.

Building a tool where AI is a helpful co-pilot for complex, long-form creativity, rather than an unpredictable text generator, presented some unique engineering and product challenges.

Engineering Controllable AI: It's All About Context & Pipeline

The biggest technical hurdle (and opportunity) was moving beyond the standard "throw a prompt at an LLM" approach. For a story spanning potentially hundreds of thousands of words, consistent characters, complex plots, and specific world rules, generic prompting falls apart fast. The AI needs deep, structured context from your world.

This led to two key architectural decisions:

1. Treating Story Data as Rich, Structured Context: This goes back to the 'filesystem' idea. Instead of just having a character name, we have a Character object with fields for traits, backstories, and crucially, their Dialogue Style. When you generate a scene with that character, the AI isn't guessing their voice; it's given the specific "How does this character speak?" description you wrote. The same applies to Configuration Files (config.genre, config.bannedwords, etc.). These aren't just suggestions; they are processed and included as explicit, structured instructions in the prompt sent to the AI. Implementing these detailed input fields and ensuring they reliably format into prompts the AI understands is a constant process of prompt engineering baked into the feature development itself.

2. The Multi-Model Optimization Pipeline: Sending a massive block of raw story data and user instructions directly to a single LLM can be inefficient and less predictable. Our pipeline was built to optimize this:

   • Your current context (scene data, active configs, character details, user instructions) is first processed.

   • This structured data is then fed to a smaller, fast model (currently Gemini Flash) whose specific job is to act as a prompt engineer. It takes the structured context and your request and crafts a highly optimized, concise, and effective prompt tailored for the final generation LLM.

   • This optimized prompt is then sent to the user-selected main AI model.

This pipeline isn't just a technical flourish; it's a product decision for control and agility. It ensures the AI consistently understands the nuanced context you've built, and it allows us to quickly integrate new powerful LLMs as they become available (sometimes literally minutes to wire in a new model) because the prompt optimization layer handles the heavy lifting of formatting the input correctly.

Getting Those First Users: Early Marketing Signals

Getting the first 200+ users onboard in the first month has been a crash course in marketing a niche AI product.

• Where they came from: Early traction has been primarily organic. Posting on relevant communities like r/WritingwithAI, r/worldbuilding, and yes, r/indiehackers, generated the initial sign-ups. Direct outreach to a few contacts interested in AI/writing helped kick things off. Paid ads haven't been tested yet.

• What messaging resonated: Initially, I focused heavily on the "AI writing tool" aspect. What resonated more was highlighting the specific problems it solves: "Stop inconsistent characters," "Manage your complex world," "Never lose a draft with versioning." Framing it as a structured storytelling tool powered by AI seems more accurate and appealing to users frustrated with chaos.

• Biggest Challenge: Discovery. The AI writing space is noisy. Cutting through that and clearly communicating the unique value proposition – the focus on structure, control, and versioning over just raw text generation – is the main hurdle. It requires potential users to understand the value of these underlying principles for their long-term projects.

• Surprises: The immediate adoption of features like "Character Dialogue Style" was a pleasant surprise. It validated that users are indeed looking for granular control over elements often overlooked in simpler tools. Also, the sheer volume of 1200+ AI generations in the first month shows that once users understand the tool, they really put it to work!

Lessons Learned (So Far):

• Solve a Specific Pain Point: Don't just build an AI wrapper. Identify a genuine, felt problem within a niche (lack of structure/control in complex creative writing) and build a targeted solution.

• Engineering as a Feature: For certain audiences (like here on Indie Hackers), the how you built it (structured data, specific AI pipeline, versioning) is part of the value proposition and differentiation.

• Messaging Matters: It's not just an "AI tool," it's a "Structured Creative Workspace powered by AI." Finding that clear language is crucial.

• Listen to Usage Patterns: The popularity of certain features (like character dialogue) is a strong signal for what users value most and helps prioritize the roadmap (e.g., doubling down on character management features, explaining less used ones like the Scratchpad better).

The journey is just beginning, and the focus remains on iterating based on how creators are actually using the platform and the feedback they provide. Adding collaboration next feels like a big step in making this structured environment even more powerful for building worlds together.

Happy to answer any questions about the tech, the early marketing grind, or the product itself!

https://aistoryhub.co