About the Project
Inspiration
The inspiration for this project came from a recurring frustration with existing productivity and planning tools: while they are effective at tracking tasks, they offer little support for thinking through long-term goals. High-level objectives are often vague, multi-constraint, and difficult to decompose, yet most tools assume users already know what to do and how to do it.
I wanted to explore whether an AI system could act as a planning partner—helping users reason about their goals before committing them to a rigid task list.
How the Project Works
The project is an AI-powered long-term planning assistant that converts natural-language conversations into structured planning artifacts.
Users first interact with an AI agent to clarify intent, constraints, and priorities. The system then generates multiple planning options. Once a plan is selected, it is deterministically transformed into a hierarchy of:
[ \text{Goal} \;\rightarrow\; \text{Milestones} \;\rightarrow\; \text{Tasks} ]
These entities are stored in a relational backend, enabling future extension to progress tracking, revision, and long-term memory.
What I Learned
Through this project, I learned that integrating LLMs into real systems is less about model capability and more about system design. Key takeaways include:
- Free-form AI output must be constrained, parsed, and validated before it can be safely persisted.
- Clear data models are essential when translating probabilistic AI reasoning into deterministic workflows.
- Building an MVP requires carefully choosing what not to automate and defining explicit fallback behaviors when AI output fails.
How I Built It
The project was implemented as a modular backend system with a clear separation of concerns:
- A conversational layer responsible for AI interaction and intent clarification
- A planning layer that parses and validates structured AI outputs
- A persistence layer that stores goals, milestones, and tasks using relational schemas
The system emphasizes API clarity, schema validation, and extensibility, rather than end-to-end automation.
Challenges & Trade-offs
One of the main challenges was balancing AI flexibility with system reliability. While LLMs excel at generating ideas, they are not inherently trustworthy for direct execution. Designing robust parsing logic and fallback mechanisms was critical to prevent malformed or inconsistent plans.
Another challenge was defining the right abstraction level for planning: overly granular tasks reduce flexibility, while overly abstract plans reduce usefulness. This led to several iterations of the goal–milestone–task hierarchy.
Reflection
This project shifted my perspective on AI-driven applications—from treating AI as a solution to viewing it as a component within a larger engineered system. It reinforced the importance of combining probabilistic reasoning with explicit structure, especially for long-horizon, real-world planning problems.
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