# Inline Validation vs --review Validation in Understand Anything: Key Differences > Understand the key differences between inline validation and --review validation in Understand Anything. Learn how each method ensures data quality for your projects. - Repository: [Egonex/Understand-Anything](https://github.com/Egonex-AI/Understand-Anything) - Tags: deep-dive - Published: 2026-06-27 --- **Inline validation runs fast, deterministic structural checks locally without LLM calls, while --review validation dispatches a Claude-powered graph reviewer for comprehensive quality analysis.** The Understand Anything repository by Egonex-AI provides two distinct validation strategies for generated knowledge graphs. The difference between inline validation and --review validation centers on speed, cost, and depth of analysis—balancing token efficiency against comprehensive AI review. ## Overview of the Two Validation Modes The orchestrator validates generated knowledge graphs through two mutually exclusive paths: - **Inline deterministic validation** (default): Executed when running `understand` without the `--review` flag - **`--review` validation**: Activated by explicitly adding the `--review` flag to your command These modes differ fundamentally in implementation, cost, and the types of errors they detect. ## Inline Deterministic Validation (Default Path) When you execute the `understand` command without the `--review` flag, the system triggers inline deterministic validation. This mode executes a built-in script that scans the graph for critical structural problems using local logic only. ### What It Checks The deterministic validator catches blocking structural errors: - **Dangling references** between nodes that point to non-existent targets - **Missing layers** in the graph hierarchy - **Duplicate IDs** that violate uniqueness constraints These checks run entirely locally, avoiding any LLM API calls. ### Implementation Details According to the source code in [`understand-anything-plugin/skills/understand/SKILL.md`](https://github.com/Egonex-AI/Understand-Anything/blob/main/understand-anything-plugin/skills/understand/SKILL.md), the default validation path is defined at line 595. The orchestrator follows this branch when `$ARGUMENTS` does not contain `--review`, executing the "Default path (no `--review`): inline deterministic validation" logic. ## --review Validation (Full LLM Review) Adding the `--review` flag switches the orchestrator to a sophisticated validation pipeline that leverages large language models for deeper analysis. ### Advanced Quality Analysis Instead of simple structural checks, the LLM reviewer—implemented as a sub-agent—analyzes the graph for higher-level quality issues: - **Orphan nodes** with no meaningful connections to the graph structure - **Naming convention suggestions** for better semantic clarity - **Summarization opportunities** to condense redundant information - **Semantic consistency** across different graph layers These insights require reasoning capabilities that deterministic scripts cannot provide. ### Implementation Details The orchestrator checks for the `--review` flag at line 678 of [`SKILL.md`](https://github.com/Egonex-AI/Understand-Anything/blob/main/SKILL.md) and branches to the "full LLM reviewer" path. This dispatches the graph-reviewer sub-agent using the prompt template stored in [`understand-anything-plugin/skills/understand/graph-reviewer-prompt.md`](https://github.com/Egonex-AI/Understand-Anything/blob/main/understand-anything-plugin/skills/understand/graph-reviewer-prompt.md). ## Token Economics and Performance The project implements a deliberate token-reduction design documented in [`docs/superpowers/specs/2026-03-27-token-reduction-design.md`](https://github.com/Egonex-AI/Understand-Anything/blob/main/docs/superpowers/specs/2026-03-27-token-reduction-design.md). By defaulting to inline validation, the system saves approximately **58,000 tokens per run** while still catching all blocking structural errors. Developers can opt-in to the expensive LLM analysis only when deeper semantic insights are necessary. ## How to Use Each Validation Mode Here are the practical ways to invoke each validation strategy: ```bash # Fast inline validation only (default) - saves ~58k tokens understand --full # Inline checks plus full LLM review understand --full --review # LLM review only on existing graph (skips deterministic checks) understand --review ``` The `--full` flag triggers a complete graph build, while the presence of `--review` determines whether the graph-reviewer sub-agent activates. ## Summary - **Inline validation** executes deterministic structural checks locally without LLM costs, catching dangling references, missing layers, and duplicate IDs as implemented in [`SKILL.md`](https://github.com/Egonex-AI/Understand-Anything/blob/main/SKILL.md) line 595. - **--review validation** invokes a Claude-powered agent for semantic quality analysis, surfacing orphan nodes and naming issues via the sub-agent defined at line 678. - The default inline path saves approximately **58k tokens per run** according to the token-reduction design specification. - Use `understand --review` to skip rebuilding and run only the LLM reviewer on existing graphs. ## Frequently Asked Questions ### When should I use --review validation instead of inline validation? Use `--review` when you need semantic quality checks beyond structural validation, such as verifying naming conventions, detecting orphan nodes, or getting summarization suggestions. Stick with inline validation for CI/CD pipelines where speed and token economy matter most, as it avoids the ~58k token overhead of the LLM call. ### Can I run the LLM reviewer without rebuilding the graph? Yes. Running `understand --review` without the `--full` flag skips the deterministic inline script and executes only the graph-reviewer sub-agent against the existing graph. This is useful when you want a quick quality audit without regenerating the entire knowledge graph. ### What specific errors does inline validation catch compared to --review? Inline validation catches deterministic structural errors like dangling references, missing layers, and duplicate IDs. The `--review` validation identifies higher-level quality issues like orphan nodes, semantic inconsistencies, and naming improvements that require LLM reasoning to detect according to the prompt defined in [`graph-reviewer-prompt.md`](https://github.com/Egonex-AI/Understand-Anything/blob/main/graph-reviewer-prompt.md). ### How much more expensive is --review validation in terms of tokens? According to the token-reduction design specification in [`docs/superpowers/specs/2026-03-27-token-reduction-design.md`](https://github.com/Egonex-AI/Understand-Anything/blob/main/docs/superpowers/specs/2026-03-27-token-reduction-design.md), the inline validation path saves approximately **58,000 tokens per run** compared to invoking the LLM reviewer. The `--review` mode consumes these additional tokens to power the Claude graph analysis.