AI-Assisted Writing Workflow
Local Qwen Chinese Fiction Pipeline
A local Qwen writing pipeline controlled by Python. It experiments with agent-like writing behavior where the model generates questions, answers them, records context, and uses that context for later fiction segments.
Period
May 2026
Tools / Tech
PythonQwenLocal LLMPrompt WorkflowContext Management
Why I built it
Project focus: this was an exploration of prompt workflow, lightweight memory design, and long-form generation control with a local model rather than a cloud API.
Links
What it includes
- Runs a local Qwen model through a Python-controlled generation loop.
- Builds a fiction workflow for setting, outline, segmented continuation, context review, and draft output.
- Uses a lightweight memory mechanism so previous questions, answers, and story context can guide later writing, with manual review for coherence.
What I worked on
- Designed the self-questioning loop where the model asks, answers, and summarizes its own context before continuing.
- Built the Python control flow for prompt sequencing, context recording, and staged generation.
- Manually reviewed generated text for coherence and kept the project framed as a writing workflow experiment.
What I Learned
- Learned how fragile long-form generation becomes when context is not explicitly managed.
- Practiced designing memory and prompt steps as a workflow instead of a single prompt.
- Built intuition for the gap between an agent-like effect and a reliable agent system.