admin
63075eaca9
- document responsibility attribution as a project-wide invariant - prevent inferred ownership in protocol rendering - add negative gold regression for false responsibility assignment - document future responsibility evidence and attribution model - record the real-life benchmark finding
235 lines
6.0 KiB
Markdown
235 lines
6.0 KiB
Markdown
# Roadmap
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No dates are assigned. Phases describe dependency order, not release promises.
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## Phase 1 - Stable Local Extraction
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Goal:
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- Establish reliable per-chunk extraction behavior for core meeting semantics.
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Deliverables:
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- Stronger Gold Standard coverage across facts, positions, decisions, todos,
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questions and technical details.
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- Gold coverage for responsibility attribution: discussion, objection, role
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proximity and department mention must not become ownership.
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- Improved category prompts.
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- Repeatable evaluation workflow.
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- Documented prompt experiment log.
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Prerequisites:
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- Existing chunk extraction flow.
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- Existing Gold Standard runner and methodology.
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Out of scope:
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- Full-transcript LLM extraction.
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- Larger context-window strategy changes without an explicit experiment.
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- Canonicalization, semantic consolidation or final protocol rendering.
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## Phase 2 - Deterministic Canonicalization
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Goal:
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- Convert independent chunk extraction JSON into a validated, normalized,
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evidence-bearing intermediate representation without semantic guessing.
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Deliverables:
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- Canonicalizer V1 implemented in Python.
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- Stable source references and IDs.
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- Normalized category names and basic field structure.
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- Safe deterministic cleanup.
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- Exact duplicate grouping where unambiguous.
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- Preservation of all source evidence.
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Prerequisites:
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- Stable local extraction baseline.
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- Agreement on the extraction object shape that should be canonicalized.
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Current status:
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- Implemented as `meeting_lab.consolidation.canonicalize`.
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Out of scope:
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- Uncertain semantic merging.
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- Topic synthesis.
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- Protocol writing.
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- LLM calls.
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## Phase 3 - Semantic Consolidation
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Goal:
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- Merge canonicalized extraction objects into a coherent semantic meeting
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representation while preserving evidence and uncertainty.
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Deliverables:
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- Semantic Consolidator V0 using the local LLM for facts-only duplicate
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detection.
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- Semantically equivalent fact statement merging.
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- Evidence preserved from all contributing chunks.
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- Complete source fact coverage validation.
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- Later broader semantic consolidation with topic grouping, contradiction and
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uncertainty markers, durable/transient separation and Canonical Meeting
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Knowledge preparation.
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Prerequisites:
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- Canonicalizer V1 output with stable IDs and source references.
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- Gold or benchmark cases that expose duplication and category shifts.
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Current status:
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- Semantic Consolidator V0 is implemented and experimentally validated for
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facts-only conservative merging.
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- The first accepted benchmark merged one correct pair among 33 facts and left
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31 singleton groups.
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Out of scope:
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- Direct protocol writing.
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- Topic synthesis in V0.
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- Processing decisions, action items, questions, positions or technical details
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in V0.
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- Canonical Meeting Knowledge generation in V0.
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- Deriving output views from one another.
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- Retrieval or RAG integration.
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## Phase 4 - Canonical Meeting Knowledge
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Goal:
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- Define and implement the semantic intermediate model that becomes the source
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of truth for downstream outputs.
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Deliverables:
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- Canonical Meeting Knowledge schema.
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- Source evidence and traceability fields.
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- Clear distinction between durable knowledge and meeting-specific actions.
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- Migration path from consolidated extraction JSON into the canonical model.
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Prerequisites:
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- Semantic consolidation behavior that preserves evidence and uncertainty.
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- Agreement on required semantic categories.
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Out of scope:
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- GUI.
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- Export formats beyond those needed to validate the model.
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- Knowledge-system storage design.
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## Phase 5 - Output Views
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Goal:
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- Render purpose-specific outputs from Canonical Meeting Knowledge without
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changing meaning.
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Deliverables:
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- Working Protocol / Arbeitsprotokoll renderer.
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- Distribution Protocol / Verteilerprotokoll renderer.
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- Knowledge Objects / Wissensdatenbankeintrag renderer or structured export.
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- Later additional views such as action lists.
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- Tests or checks showing that output views are parallel renderings of the same
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canonical model.
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- Default output-language policy: rendered protocols normally match the
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dominant source language unless explicitly requested otherwise.
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Prerequisites:
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- Implemented Canonical Meeting Knowledge.
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- Clear audience and completeness rules for each output view.
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Out of scope:
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- Additional analysis during rendering.
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- Deriving one output view from another.
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- Retrieval integration.
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## Phase 6 - Review and Quality Control
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Goal:
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- Add optional review stages that improve omission detection, consistency and
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model selection.
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Deliverables:
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- Optional whole-transcript review.
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- Omission detection.
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- Consistency checks.
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- Model comparison workflow.
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- Hardware and runtime benchmarks.
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Prerequisites:
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- Stable extraction, semantic consolidation and canonical model.
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- Representative test meetings.
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Out of scope:
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- Automatic acceptance of review suggestions without evidence.
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- Product UI work.
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- Cloud deployment.
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## Phase 7 - Productization
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Goal:
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- Turn the validated pipeline into a usable local workflow.
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Deliverables:
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- Recording/transcription workflow.
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- FFmpeg integration.
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- Meeting metadata capture.
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- Participant entry.
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- GUI.
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- Stable deployment process.
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- Export workflows.
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Prerequisites:
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- Stable pipeline stages and output views.
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- Clear operational requirements for local use.
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Out of scope:
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- Enterprise knowledge retrieval.
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- Future Meeting Assistant integration beyond export contracts.
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- Cloud-first architecture.
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## Phase 8 - Knowledge-System Integration
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Goal:
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- Reuse durable meeting knowledge in broader knowledge systems.
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Deliverables:
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- Structured Knowledge Objects.
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- Retrieval-ready storage format.
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- Future RAG integration path.
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- Reuse contracts for Meeting Assistant and other knowledge systems.
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Prerequisites:
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- Canonical Meeting Knowledge and Knowledge Objects are implemented and stable.
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- Durable knowledge is separated from meeting-specific actions and discussion
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history.
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Out of scope:
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- Building a full enterprise search product inside Meeting Lab.
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- Treating raw transcripts or generated protocols as the knowledge source of
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truth.
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