Comparison & Recommendation

Deltas between approaches plus a recommended adoption path.

Accordions Charts (SVG) Icons Enhanced images

Side-by-side delta

Evidence-based
Summary: Approach 2 is the target deterministic architecture; Approach 1 is a transition architecture used to accelerate discovery, with strict promotion/expiry rules for LLM output.

DimensionApproach 1 — LLM‑First (Controlled)Approach 2 — Deterministic‑FirstDelta
Role of LLMs Early semantic bootstrapping, provisional outputs with expiry & promotion gates. Non-authoritative; optionally for naming/explanation; never defines truth. A1 trades faster discovery for added governance controls.
Replayability Required eventually; LLM outputs must be retired/promoted. Mandatory at every layer (schema enforcement, deterministic ingestion, rules/ML). A2 stronger audit posture.
Governance model Promotion pipeline: LLM → human validation → rules/ML → authoritative DB. Human-in-the-loop on semantics; no self-modifying behavior. Both require governance; A1 adds explicit transition controls.
Time-to-value Faster demos and taxonomy discovery in early phases. Slower initial modeling, but stable baseline early. A1 best for early discovery; A2 best for regulated scale.

Illustrative scoring chart

A visual summary aligned with both documents (scores are illustrative, not measured).

Comparison bars

Recommendation

Best for devs & org
👩‍💻Best approach for developersPragmatic

Start with Approach 2 as the backbone (contracts, schemas, replayable pipeline), and optionally add the Approach 1 bootstrap layer as an offline accelerator where uncertainty is high.

  • Keep ingestion + schema strictly deterministic.
  • Use LLMs only for summarization/naming and only if outputs have expiry + promotion workflow.
  • Promote learnings into rules DSL / ML models and retire the LLM outputs.
🏢Best approach for the organisationAudit-ready

Approach 2 is the organisational default (auditability, compliance, determinism, predictable cost). Use Approach 1 only as a controlled transition state with explicit gates.

  • Semantic layer remains governed core IP.
  • No LLM output can be production-authoritative without a deterministic successor.
  • Agents remain read-only and recommendation-only.
🧾Decision rule (industry-standard)Simple gate

If an output must be reproducible / auditable / compliance-grade, it must be produced by deterministic rules/ML — not an LLM.