From Blobs to Blocks: Componentizing LLM Output for Real Work

TL;DR

Most LLM tools hand you a blob. Componentization treats an answer as parts—headings, paragraphs, code blocks, steps, or JSON subtrees—with stable IDs and links. You can edit, switch on/off, or regenerate any part, then recompose the final artifact. In early tests, this aligns with how teams actually work: outline first, keep the good bits, surgically fix the bad ones, and reuse components across docs. It’s a small idea with big downstream benefits for control, auditability, and collaboration.

Why “monolithic chat” fails at real work

• You either copy–paste into another editor (losing conversational context) or re‑prompt and risk global, unintended changes.
• In multi-stakeholder deliverables (decks, RFPs, SOPs, PRDs), people naturally work piece‑by‑piece—not as one fragile wall of text.

Quick contrast

The core idea, in plain English

1. Decompose a model’s reply into semantic components (e.g., Subject, Greeting, Paragraph; or Function, Import, Test).
2. Let users Edit / Toggle / Regenerate at the component level.
3. Recompose a final document that reflects just the parts you want.

The paper formalizes this with three principles:

• MAOD (Modular & Adaptable Output Decomposition): a semantic pass that identifies logical units and links among them (not just paragraph splitting).
• User‑driven manipulation: inline editing, include/exclude, targeted rewrites.
• Dynamic recomposition: produce the final output from the current set of included components.

A minimal schema looks like this:

A reference system you could build (or buy)

The prototype, MAODchat, uses a microservices layout:

• Frontend: a four‑column interface (prompt → monolithic reply → components → recomposed output) with real‑time toggles.
• Backend: vendor‑agnostic model adapters via a dynamic factory pattern; conversation state in a persistent store.
• MAOD Agent: a state‑machine (e.g., Parse → Decompose → Validate) that returns a typed DecomposedResponse.
• Protocols: Agent‑to‑Agent messaging (A2A) for clean decomposition tasks; easy to extend with fact‑checkers or formatters later.

Why this matters to CIOs/Heads of Product

• Resilience: a broken bit is localized. No need to regenerate the whole deliverable.
• Governance: components enable role‑based approvals, provenance, and audit trails at the part level.
• Reuse: libraries of components (e.g., boilerplate risk disclosures, standard feature blurbs) become real assets.

What we’d do at Cognaptus in a 30‑day pilot

Scope: pick one high‑leverage document family that already has sections—e.g., sales proposals, compliance SOPs, or engineering RFCs.

Pilot plan

1. Week 1 – Fit & decomposition quality

   • Label 50 sample docs into components; evaluate MAOD precision/recall on types and links.
   • Define a component taxonomy (e.g., Intro, Problem, Proposed Solution, Pricing, Assumptions, Legal).

2. Week 2 – UX & team flow

   • Ship a 2‑pane MVP (avoid 4‑column cognitive load): Components on the left; Recomposed preview on the right.
   • Gate per‑component actions: Edit (inline), Toggle, Regenerate.

3. Week 3 – Governance & reuse

   • Add owners per component type (e.g., Legal owns “Terms”).
   • Create a component library with version tags; allow insert/search.

4. Week 4 – Metrics & rollout

   • Measure time‑to‑first‑draft, edit counts, and reduction in full regenerations.
   • Decide scope expansion and integration points (Docs, Confluence, Jira, Git).

Success metrics (directional targets)

• −40–60% time to reach an approved draft.
• −60–80% full‑document regenerations.
• +30–50% reuse of standard components across artifacts.
• Reviewer NPS +20 relative to baseline.

Concrete enterprise use‑cases

• RFP/Proposal engines: Maintain a curated library of components (win‑themes, compliance statements, pricing notes). Auto‑compose, then tailor only the relevant blocks.
• Compliance & Legal: Canonical clauses as components; red‑line at the component level; keep history by clause ID.
• Engineering change logs: RFC sections as components with owners; merge strategies feel like Git for docs.
• Customer Support macros: Troubleshooting steps as toggleable components; regenerate only step 4 when the product changes.

Risks & how to mitigate them

Decomposition errors. If the agent splits poorly, editing is harder than before.

• Mitigate: human‑in‑the‑loop labeling; confidence scores; “merge/split” manual controls.

Formatting fidelity. Markdown and list structure can be lost in decomposition/recomposition.

• Mitigate: enforce typed blocks; add snapshot‑based diff tests; round‑trip fuzzing.

Latency. Decomposition adds overhead.

• Mitigate: stream the monolithic reply first, then progressively reveal components; cache models; async DB.

Cross‑component coherence. Edits in one section can misalign others.

• Mitigate: lightweight dependency graph; flags when referenced claims drift; optional “global reconcile” pass before publish.

Build/buy checklist

• Typed schema with stable IDs and links
• Inline Edit / Toggle / Regenerate actions
• Component library (search/insert/version)
• Ownership & approvals per component type
• Provenance (source prompt, model, time)
• Round‑trip formatting tests (markdown, code, tables)
• Diff/merge at component granularity
• Exporters (Docx, PDF, Markdown, HTML)

My take

Componentization sounds mundane—just “parts.” But parts are what make software and supply chains scale. Bringing that logic to AI outputs unlocks control, reuse, and governance. If your AI docs still arrive as blobs, you’re leaving velocity and auditability on the table.

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Cognaptus: Automate the Present, Incubate the Future