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active / program / LLM Systems / 2025 -> now

Career Survey Intelligence & Compensation Consistency Engine

Structured extraction and grounded consistency checks made compensation and leveling review explainable.

System fingerprint

Select a signal node to inspect its operating principle.

  1. ExtractionNormalize free text into structured signals.
  2. GroundingCombine models, rules, and domain constraints.
  3. ConsistencySurface leveling and compensation conflicts.
  4. Human reviewAttach rationales and traceable evidence.

01 / Evidence

Outcome

  • Parsed and normalized free-text roles and narratives into structured signals.
  • Detected compensation and seniority inconsistencies with explainable rationales.
  • Supported multi-regional review across leveling, role definitions, and compensation.

02 / Operating environment

Context

LLM-augmented analytics + optimization for high-stakes workflows.

Teams operating high-stakes compensation and leveling workflows

03 / Boundary conditions

Constraints

  1. C01

    Accuracy and auditability matter more than cleverness.

  2. C02

    Free-text survey content and job descriptions must become structured signals.

  3. C03

    Review decisions require rationales and traceable evidence.

04 / Decision path

Architecture

Select a signal node to inspect its operating principle.

  1. ExtractionNormalize free text into structured signals.
  2. GroundingCombine models, rules, and domain constraints.
  3. ConsistencySurface leveling and compensation conflicts.
  4. Human reviewAttach rationales and traceable evidence.

05 / Engineering choices

Key decisions

  • D1Use LLMs as one component in a hybrid quantitative, rules, and grounding system.
  • D2Apply domain constraints to ground LLM outputs.
  • D3Keep human review in the decision workflow.

06 / Proof discipline

Evaluation

  • Version prompts and maintain test sets.
  • Run regression checks as prompts or system components change.
  • Monitor outputs and retain explainable rationales for review.

07 / Scope

Ownership

LLM pipeline, consistency detection, optimization, and evaluation engineering

  1. O01

    Structured extraction from survey content and job descriptions

  2. O02

    Consistency detection across multi-regional datasets

  3. O03

    Grounding, evaluation, monitoring, and human-review workflow

08 / Explicit compromises

Trade-offs

  • Prefer a hybrid grounded system over an LLM-only design.
  • Prefer accuracy and auditability over cleverness.

Implementation stack

Python / LLM pipelines / Optimization / Hybrid rules+ML / Evaluation/monitoring

From evidence to delivery

Related production services

  • Production AI and LLM systems

    The documented extraction, grounding, evaluation and human-review controls are relevant to production system design.

  • RAG and retrieval systems

    The work demonstrates grounding and evaluation mechanics. It is not presented as a client RAG deployment.

  • AI prototype to production

    Evaluation gates and explicit human review show the hardening discipline used when a prototype becomes an owned service.

Next evidence

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