The AI Project Planner is a web application designed to help novices navigate credit-based AI platforms by simplifying project planning, prompt creation, and resource management. It addresses common beginner struggles like understanding AI costs and capabilities, and crafting effective prompts. Core features guide users through defining project goals (A1), offer dedicated areas for prompt writing (B1) with quality checklists (B3) and heuristic feedback on complexity (C1), provide in-context explanations of AI terms (D1), and allow text extraction from documents for use in prompts (E1). The system aims to build user confidence and enable more systematic and economical use of AI tools through a supportive, educational, and clearly structured experience.
Here’s a summary and description of the files and the project:
File Summaries:
1. AI Project Planner – Style Guide.docx (Word Count: ~2,860)
This guide defines the “AI Project Planner’s” visual and content principles, emphasizing a user-friendly, accessible, high-definition monochrome aesthetic (white/light on black/dark) to empower novice users. It covers color palette, typography, iconography, layout, UI component styling (buttons, forms, modals, tooltips), a supportive and clear tone of voice, WCAG 2.1 AA accessibility, and alignment with TowerIO branding.
2. AI Project Planner – Detailed Project Summary.docx (Word Count: ~1,450)
This document details the “AI Project Planner’s” purpose: to help novices plan AI projects, especially on credit-based platforms, by guiding goal breakdown, prompt crafting, offering complexity feedback, and education. It addresses novice struggles with AI resource consumption, planning, and prompt engineering. The blueprint was developed iteratively, integrating user-provided research. Key features include guided goal definition (A1), prompt input areas (B1), and heuristic feedback (C1).
3. AI Project Planner – Build Log & Research Notes.docx (Word Count: ~2,270)
This log chronicles the “AI Project Planner’s” blueprint development, from initial problem identification (novice difficulty with credit-based AI) to a comprehensive text-based blueprint. It details the iterative refinement of research goals, the pivotal shift to integrating user-provided research documents, and the systematic generation of user stories, requirements (FRs/NFRs), and textual UI specifications for prioritized features.
4. Novice AI Project Goal Research_.docx (Word Count: ~10,130)
This research analyzes online discussions (primarily Reddit) to understand novice user challenges in defining AI project goals. Key findings show novices struggle with problem formulation, AI capability mapping, cost implications, and desire structured, mentor-like guidance with templates and clear explanations. They use non-technical language and need help translating ideas into AI specifications. Recommendations focus on a wizard-style goal definition feature addressing these issues.
5. AI Project Planner Blueprint Research_.docx (Word Count: ~11,960)
This document outlines methodologies for creating the AI Project Planner’s textual blueprint, focusing on feature specification and UI/UX conceptualization for novice users. Part 1 covers user stories (with an educational angle), functional requirements (FRs) for AI features (input, output principles, feedback), and non-functional requirements (NFRs) like usability and accessibility. Part 2 details adaptable UI/UX patterns (wizards, hierarchical lists, structured forms, feedback panels), methods for textually describing user flows (narrative, structured steps, Gherkin), and specifying conceptual UI structure, information hierarchy, and interactions textually, ensuring novice-friendliness.
6. AI Project Planner Feature Strategy_.docx (Word Count: ~12,130)
This document outlines strategies for defining, prioritizing, and developing the “AI Project Planner’s” features to empower novices. It details feature identification (User Story Mapping, JTBD), cataloging, and a hybrid prioritization approach (MoSCoW/Kano for MVP; RICE/Value vs. Effort for enhancements; ongoing Opportunity Scoring), emphasizing novice needs. It describes systematically gathering and analyzing Reddit data (thematic analysis, NLP) to inform prioritization. MVP definition focuses on core problem-solving and a Minimum Viable Experience (MVE). It also covers strategic integration of in-app educational features and balancing ambition with development constraints via phased rollouts.
7. blueprint.txt (Word Count: ~16,070)
This is the detailed application blueprint for the “AI Project Planner,” designed to empower novices using credit-based AI. It categorizes features (Must-Have, Should-Have, Could-Have) and provides specifications for each, including User Stories, Functional Requirements (FRs), Non-Functional Requirements (NFRs), and Textual UI Flow/Element Specifications. Detailed “Must-Have” features include A1: Guided Project Goal Definition, B1: Dedicated Prompt Input Area, B3: Basic Prompt Quality Checklist, C1: Simplified Heuristic Prompt “Weight” Indicator, D1: Contextual Tooltips, E1: Plain Text File Upload/Extraction, F1: User Registration/Login, and F2: Project Saving/Loading.
Okay, here is a brief summary for that file, formatted as #8 to match the style of the previous concise list:
8. AI Project Planner – Blueprint notes – User story documentation.docx (Word Count: ~16,070)
This is the detailed: User Stories, Functional Requirements (FRs), Non-Functional Requirements (NFRs), and Textual UI Flow/Element Specifications. Key “Must-Haves” like Guided Goal Definition (A1), Prompt Input Areas (B1), Quality Checklists (B3), “Prompt Weight” Indicator (C1), Tooltips (D1), File Upload/Extraction (E1), User Auth (F1), and Project Save/Load (F2) are detailed.