6.0 KiB
Infospace Bench Product Requirements Document V0.1
infospace-bench
1. Product Overview
1.1 Product Name
infospace-bench
1.2 Product Definition
infospace-bench is a workspace and service for creating, developing, evaluating, and inspecting structured knowledge spaces (“infospaces”).
It provides an environment for applying structured knowledge methods to real-world domains such as books, research corpora, projects, and organizational knowledge systems.
2. Product Intent
2.1 Problem Statement
While tools and systems for structured knowledge exist:
- There is no consistent way to develop and evaluate knowledge spaces in practice
- Knowledge engineering approaches remain abstract and hard to validate
- Real-world knowledge artifacts lack structured inspection and quality evaluation
- Iterative refinement of knowledge systems is often ad-hoc and untraceable
This results in a gap between knowledge theory and applied knowledge systems.
2.2 Intended Outcomes
infospace-bench enables:
- Creation of explicit, structured knowledge spaces as first-class artifacts
- Iterative development, evaluation, and refinement of knowledge collections
- Inspection of structure, quality, and relationships within knowledge
- Application of AI-assisted workflows to real-world knowledge problems
- Generation of reusable patterns and insights for knowledge engineering
2.3 Success Criteria
The product is successful when:
- Infospaces can be created, evolved, and inspected systematically
- Knowledge quality can be evaluated and improved over time
- Real-world use cases (books, domains, projects) can be modeled effectively
- AI agents can contribute meaningfully to knowledge development workflows
- Insights from infospaces inform improvements in lower-layer systems
3. Scope Definition
3.1 In Scope
- Definition and management of infospaces as structured knowledge collections
- Lifecycle management: creation, population, evaluation, refinement, export
- Inspection tools for structure, relationships, and quality
- Application of workflows (generation, transformation, analysis) to infospaces
- AI-assisted development and evaluation of knowledge artifacts
- Use-case-specific configurations and project setups
3.2 Out of Scope
- Low-level markdown processing or transformation primitives
- Persistent system infrastructure or orchestration engines
- Generic multi-format content management systems
- Reusable tooling libraries or platform-level abstractions
- Final production publishing platforms (outside knowledge development context)
3.3 Boundary Clarification
infospace-bench provides application-level usage, not infrastructure:
- Tooling primitives →
markitect-tool - System/runtime orchestration →
kontextual-engine
4. Functional Expectations
4.1 Core Capabilities
The product must support:
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Infospace Definition Create structured knowledge collections with clear boundaries
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Population & Development Add, modify, and organize knowledge artifacts
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Evaluation & Quality Assessment Measure structure, consistency, and completeness
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Inspection & Visualization Explore relationships, dependencies, and structure
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Transformation & Generation Apply workflows to evolve knowledge artifacts
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Export & Representation Produce outputs (documents, reports, derived artifacts)
4.2 Interaction Modes
- Project/workspace-based interaction
- CLI and/or service interfaces
- Agent-assisted workflows
5. Non-Functional Expectations
5.1 Performance
- Efficient handling of medium-to-large knowledge collections
- Reasonable responsiveness for interactive inspection and iteration
5.2 Reliability
- Consistent behavior in evaluation and transformation workflows
- Traceable changes and reproducible results
5.3 Extensibility
- Ability to incorporate new workflows and evaluation methods
- Flexible configuration for different domains and use cases
5.4 Usability
- Clear structure for organizing knowledge projects
- Intuitive workflows for iteration and refinement
- Transparency of system behavior and results
6. Assumptions and Dependencies
6.1 Assumptions
- Knowledge engineering benefits from iterative, project-based development
- AI agents can assist in generating and refining knowledge
- Structured knowledge systems require evaluation and inspection
6.2 Dependencies
- kontextual-engine for persistence and orchestration
- markitect-tool for markdown-based structuring and transformation
- llm-connect (or equivalent) for AI-assisted workflows
7. Constraints
- Must remain focused on concrete infospaces, not abstract infrastructure
- Must avoid duplication of tooling or system-level responsibilities
- Must support both human and AI-driven workflows
- Must maintain clear traceability of knowledge evolution
8. Risks
- Drifting into general-purpose platform or CMS functionality
- Over-complex evaluation frameworks reducing usability
- Tight coupling to specific tools or formats
- Loss of clarity between experimentation and production usage
9. Related Systems
- markitect-tool – syntax layer (markdown primitives)
- kontextual-engine – system layer (persistence and orchestration)
- llm-connect – LLM abstraction layer
10. Ecosystem Context
This product is part of a layered knowledge system:
markitect-tool → makes markdown structured and manipulable
kontextual-engine → makes knowledge persistent and operable
infospace-bench → makes knowledge concrete and meaningful
Layers:
- Syntax layer → markitect-tool
- System layer → kontextual-engine
- Application layer → infospace-bench
11. PRD Type
Hybrid / Boundary PRD
This PRD defines application-level intent and usage boundaries while allowing flexibility for experimentation and iterative development.