Seedlabs

Interactive-Fiction Prompt Chain Kit

A kit for game writers to turn story bibles into consistent AI prompts for branching narratives.

A toolkit for narrative game writers that compiles a story bible into a chain of constrained ChatGPT prompts — scene setup, player-choice handling, and continuation — with token budgets and temperature presets that keep AI narration consistent in tone and length across branches.

Computer ScienceTopic Modeling
Cross-Corpus Speech Emotion RecognitionInteractive fiction and narrative game development

Concept

Interactive-fiction and narrative-game writers want AI narration that stays in voice, respects world rules, and fits a length budget — raw ChatGPT reliably gives them none of that. This kit lets a writer import their story bible (characters, tone, world rules, forbidden plot beats) and compiles it into a prompt chain: a setup prompt that injects only the scene-relevant world state, a choice-handling prompt with explicit constraints on valid branches, and a continuation prompt with a per-scene token budget and a low-temperature preset for consistency (plus a high-temperature preset for brainstorming branches). Writers tune the chain and compare side-by-side outputs, iterating per the guide's refinement loop. The wedge: make AI narration a controllable instrument for branching stories rather than a slot machine.

Why now

The new guide [0] includes an interactive-storytelling case study and documents exactly the controls this kit productizes — prompt chaining, temperature and token control, explicit constraints, and iterative refinement — so the design follows published best practice rather than folklore. The LLM survey [2] charts the capability level of current models that makes generated narration usable at all, while studios shipping large volumes of branching text face tone drift and branch inconsistency as their binding production constraint — precisely what constrained, budgeted prompt chains address.

Written by GLM · 9 Oct 2026

AI assessment

Backed by 2 papers77

A focused developer tool that productizes prompt engineering best practices for a high-value niche of narrative game designers.

Evidence strength
4/5
The idea directly maps the 'best practices' (temperature, token limits, constraints) from the prompt engineering guide to a specific workflow for interactive fiction.
Market pull
3/5
While the named beneficiaries are high-profile studios, the total addressable market for professional narrative game writers is relatively small.
Novelty & moat
3/5
It is a specialized application of prompt auditing/chaining; while similar to the Support-Desk Auditor, it targets a creative workflow rather than a corporate one.
Feasibility
5/5
The tool is essentially a wrapper around existing LLM APIs and a configuration UI, making it highly buildable for a small team.
Wedge clarity
4/5
The 'story bible to prompt chain' pipeline is a sharp, specific entry point that solves a concrete pain point for writers.
Simplicity / focus
5/5
The product is a single, well-defined toolkit with a clear purpose, avoiding the 'platform' trap.

Scored by Gemma 4 · 9 Oct 2026. Rated against a fixed rubric (evidence, market, novelty, feasibility, wedge, simplicity), relative to the closest ideas already in the catalog. A prior estimate to compare ideas before real-world signal arrives.

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Who benefits

  • Latitudecompany

    Makes AI Dungeon, where prompt quality and parameter settings directly shape story output; a chain kit with token and temperature presets targets its core consistency problem.

  • Choice of Gamescompany

    Publishes large volumes of branching interactive fiction where tone consistency across branches is a real production constraint; constrained prompt chains would speed drafting while protecting each title's voice.

  • Inklecompany

    A narrative-game studio that already builds writer tools; a story-bible-to-prompt-chain compiler extends its tooling direction for prototyping AI-assisted scenes.

  • Failbetter Gamescompany

    Maintains long-running narrative worlds such as Fallen London where tone drift is costly; budgeted, constrained continuation prompts keep generated text on-voice.

Research it builds on

  1. A Survey of Large Language Models
    Wayne Xin Zhao, Kun Zhou, Junyi Li et al. · 2026 · 1422 citations
    All ideas from this paper →
  2. Prompt Engineering for ChatGPT: A Quick Guide to Techniques, Tips, and Best Practices
    Sabit Ekin · 2026 · 257 citations
    All ideas from this paper →
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feasibility