HaithimNasher · Oct 22, 2026 · Oct 22, 2026
diff --git a/README.md b/README.md
@@ -33,17 +33,17 @@ DevSecOps, observability, and Kubernetes-oriented deployment.
 
 <!-- HSAAI-PROJECT-LEADERSHIP:END -->
 
-## Validated Enterprise AI Quality
+## Validated Enterprise AI Quality — Historical Benchmark & Current Runtime
 
 | Capability | Validated result |
 | --- | --- |
 | Live Local LLM | ✅ Ollama + qwen2.5:7b-instruct |
 | Enterprise RAG | ✅ End-to-End validated |
-| Retrieval Precision | ✅ 100% on current controlled benchmark |
-| Retrieval Recall | ✅ 100% on current controlled benchmark |
+| Retrieval Precision | ✅ 100% — historical controlled benchmark |
+| Retrieval Recall | ✅ 100% — historical controlled benchmark |
 | Citation Precision | ✅ 100% |
 | Citation Recall | ✅ 100% |
-| Prompt Injection Accuracy | ✅ 100% on current benchmark |
+| Prompt Injection Accuracy | ✅ 100% — historical controlled benchmark |
 | LLM Grounded Rate | ✅ 100% |
 | Required Fact Recall | ✅ 86.61% |
 | Retrieval Security Gate | ✅ PASS |
@@ -57,8 +57,10 @@ Ollama, Keycloak, FastAPI, Next.js, PostgreSQL, Redis, Kafka, Neo4j, MinIO,
 Vault, MLflow, Prometheus, Grafana, Loki, Tempo, Thanos, Docker, and Kubernetes
 in one enterprise AI architecture.
 
-> Benchmark percentages describe the current controlled HSAAI evaluation suite
-> and are not claims of universal AI accuracy.
+> The numerical AI quality metrics above are from the previously documented
+> HSAAI v4.0.0 controlled benchmark. They are not newly measured v4.1.0
+> results or guarantees of universal AI accuracy.
+> The v4.1.0 release has separate Enterprise RAG and Live LLM CI evidence.
 
 ## العربية
 
@@ -122,6 +124,30 @@ The six successful workflows validate the release commit in
 the repository CI environment. They do not independently
 establish acceptance on an external production Kubernetes cluster.
 
+### Post-release Backend Coverage Evidence
+
+The following backend coverage result was obtained from a
+successful post-release GitHub Actions workflow.
+
+| Area | Verified Result |
+| --- | --- |
+| Validation Scope | Post-release branch validation |
+| Backend Coverage | 80.06% |
+| Required Coverage Threshold | >=80% |
+| Coverage Gate | PASS |
+| Covered Statements | 13,940 |
+| Missing Statements | 3,472 |
+| Total Statements | 17,412 |
+| Validation Commit | `9dfa666197ad46e51999e6ef9853daff68140fac` |
+| Workflow | HSAAI Full Backend Coverage |
+| GitHub Actions Run | `39803990414` |
+
+[View Backend Coverage Evidence](https://github.com/HSA-AI/HSAAI/actions/runs/39803990414)
+
+This result belongs to a post-release validation commit.
+It must not be represented as coverage measured directly
+on the published v4.1.0 release commit.
+
 ### Previous Release Evidence — v4.0.0
 
 | Area | Historical Verified Result |

diff --git a/docs/ENTERPRISE_AI_FAQ.md b/docs/ENTERPRISE_AI_FAQ.md
@@ -40,7 +40,8 @@ HSAAI Enterprise RAG includes:
 
 ## What are the current HSAAI AI quality results?
 
-On the current controlled live benchmark:
+The following numerical results are from the previously documented
+HSAAI v4.0.0 controlled benchmark, not a newly measured v4.1.0 evaluation:
 
 - Retrieval Precision: 100%
 - Retrieval Recall: 100%
@@ -52,7 +53,7 @@ On the current controlled live benchmark:
 - Overall AI Quality Gate: PASS
 - Errors: 0
 
-These values apply to the current controlled evaluation suite and should not be interpreted as universal AI accuracy.
+These historical benchmark results are not guarantees of universal AI accuracy. HSAAI v4.1.0 has separate Enterprise RAG End-to-End and authenticated Live LLM quality CI validation.
 
 ## Does HSAAI support Arabic Enterprise AI?
 

diff --git a/docs/TECHNICAL_PORTFOLIO.md b/docs/TECHNICAL_PORTFOLIO.md
@@ -112,7 +112,7 @@ The [repository overview](https://github.com/HSA-AI/HSAAI#validated-enterprise-a
 | LLM grounded rate | 100% |
 | Required fact recall | 86.61% |
 
-These percentages describe the controlled evaluation suite. They are not guarantees for arbitrary documents, questions, or deployment environments.
+These percentages belong to the previously documented HSAAI v4.0.0 controlled benchmark. They are not newly measured v4.1.0 results or guarantees for arbitrary documents, questions, or deployment environments. The v4.1.0 release has separate live runtime validation evidence.
 
 To reproduce live evaluation, open GitHub Actions → **HSAAI Live LLM Answer Quality** → **Run workflow**. Select the intended review branch and record the resolved commit.
 

diff --git a/docs/reports/CURRENT_RELEASE_STATUS.md b/docs/reports/CURRENT_RELEASE_STATUS.md
@@ -29,6 +29,26 @@ These results apply to the repository validation environment.
 They do not independently establish successful deployment
 to an external production Kubernetes cluster.
 
+## Historical AI Quality Benchmark
+
+The following numerical AI quality metrics were reported
+for the previously documented HSAAI v4.0.0 controlled benchmark:
+
+| Metric | Historical Result |
+| --- | --- |
+| Retrieval Precision | 100% |
+| Retrieval Recall | 100% |
+| Citation Precision | 100% |
+| Citation Recall | 100% |
+| Prompt Injection Accuracy | 100% |
+| LLM Grounded Rate | 100% |
+| Required Fact Recall | 86.61% |
+| Overall AI Quality Gate | PASS |
+| Benchmark Errors | 0 |
+
+These are historical controlled-benchmark results, not
+new measurements for the published v4.1.0 release.
+
 ## Previous Release Evidence — v4.0.0
 
 The following results belong to the previous production release