docs: surface explainability scope note near the top of the README

Moves a concise version of the system-level vs. foundation-model
explainability clarification up next to the opening pitch, so it's
visible before readers scroll to the high-stakes-domains section.

README.md

@@ -52,6 +52,8 @@ Most AI agents act without a trail. They store embeddings, not meaning: context
 
 Semantica sits underneath your LLM, vector store, and agent framework as a deterministic infrastructure layer: no LLM required for graph construction, reasoning, or provenance.
 
+> ⚠️ **System-level explainability, not foundation-model explainability.** Semantica does not expose or reconstruct what happens *inside* the LLM — its internal reasoning or chain-of-thought stays opaque, as it does for any external system. Semantica explains what's *outside* the model: the context and data fed in, the decision produced, its provenance, relevant relationships, applied policies, and the full execution trail.
+
 **Who it's for:**
 
 - **AI/ML platform teams** shipping agents that make consequential decisions and need structured, queryable context built from fragmented raw data, not just a vector index
@@ -1498,6 +1500,8 @@ Semantica is designed for environments where AI outputs must be explainable, aud
 - **Cybersecurity:** Threat attribution, incident response timelines, and IOC provenance tracking
 - **Autonomous Systems:** Decision logs, safety validation, and explainable AI for certification
 
+> ⚠️ **This is system-level explainability, not foundation-model explainability.** Semantica does not expose, reconstruct, or explain what happens *inside* the LLM/foundation model — its internal reasoning or chain-of-thought stays opaque, as it does for any external system. What Semantica explains is *outside* the model: the context and data fed in, the decision produced, its provenance, the relevant relationships, the policies applied, and the full execution trail. In short, Semantica explains and audits what the AI system did, not the LLM's private internal reasoning.
+
 ---
 
 ## Installation

docs/concepts.md

@@ -16,6 +16,9 @@ At its core, Semantica adds a **context and accountability layer** on top of you
 - **Accountability Layer** — Provenance tracking, decision intelligence, conflict detection, and W3C PROV-O compliance make every claim in your AI stack auditable and explainable.
 - **Extension Layer** — `PluginRegistry` and `MethodRegistry` let you replace or augment any component: ingestors, extractors, reasoning engines, backends: without changing framework code.
 
+<Warning>
+  **This is system-level explainability, not foundation-model explainability.** Semantica does not expose, reconstruct, or explain what happens *inside* the LLM/foundation model — its internal reasoning or chain-of-thought stays opaque, as it does for any external system. What Semantica explains is *outside* the model: the context and data fed in, the decision produced, its provenance, the relevant relationships, the policies applied, and the full execution trail. In short, Semantica explains and audits *what the AI system did*, not the foundation model's private internal reasoning.
+</Warning>
 
 ## Knowledge Graphs
 

docs/faq.md

@@ -52,6 +52,16 @@ Semantica works alongside these frameworks, not against them.
 
 </Accordion>
 
+<Accordion title="Does Semantica explain an LLM's internal reasoning or chain-of-thought?" icon="triangle-exclamation">
+
+No. This is **system-level explainability, not foundation-model explainability**. Semantica does not expose, reconstruct, or explain what happens *inside* the LLM/foundation model — its internal reasoning or chain-of-thought stays opaque, as it does for any external system.
+
+What Semantica explains is *outside* the model: what context and data were used, what decision was produced, the provenance behind it, the relevant relationships, the policies applied, and the resulting decision trail.
+
+In short: Semantica explains and audits *what the AI system did* — not the foundation model's private internal reasoning.
+
+</Accordion>
+
 <Accordion title="Is Semantica free?" icon="tag">
 
 Yes: MIT licensed, no vendor lock-in, no paywalled features. Some capabilities require third-party API keys (e.g., OpenAI embeddings, Groq inference), but Semantica itself is always free and open source.

docs/index.md

@@ -192,7 +192,11 @@ decision_id = context.record_decision(
 
 ## Built for Where Mistakes Have Consequences
 
-Semantica was designed for domains where every decision must be explainable and every fact must be traceable:
+Semantica was designed for domains where every decision must be explainable and every fact must be traceable.
+
+<Warning>
+  **This is system-level explainability, not foundation-model explainability.** Semantica does not expose, reconstruct, or explain what happens *inside* the LLM/foundation model — its internal reasoning or chain-of-thought stays opaque, as it does for any external system. What Semantica explains is *outside* the model: the context and data fed in, the decision produced, its provenance, the relevant relationships, the policies applied, and the full execution trail. See [Core Concepts](concepts) for the full scope note.
+</Warning>
 
 **Healthcare & Life Sciences**
 - Clinical decision support with full audit trails