diff --git a/plugins/data-designer-retrieval-sdg/README.md b/plugins/data-designer-retrieval-sdg/README.md
index 426f39d..f5d8921 100644
--- a/plugins/data-designer-retrieval-sdg/README.md
+++ b/plugins/data-designer-retrieval-sdg/README.md
@@ -20,6 +20,31 @@ registries via `[project.entry-points."data_designer.plugins"]`:
 Both ship with the same `pip install data-designer-retrieval-sdg` and
 become discoverable automatically through Python entry points.
 
+## Recipe
+
+When installed with a Data Designer version that supports recipes, this
+package also registers a `retrieval-sdg` recipe through
+`[project.entry-points."data_designer.recipes"]`. The recipe lets users run
+the opinionated end-to-end pipeline from the main Data Designer CLI:
+
+```bash
+data-designer run-recipe retrieval-sdg --config retrieval-sdg.yaml --num-records 200
+```
+
+Minimal recipe config:
+
+```yaml
+input_dir: ./my_documents
+generated_output_dir: ./generated_output
+corpus_id: my_corpus
+```
+
+Inspect the full config schema with:
+
+```bash
+data-designer recipes show retrieval-sdg
+```
+
 ## Native async (`DATA_DESIGNER_ASYNC_ENGINE=1`)
 
 `embedding-dedup` implements `agenerate()` directly on top of
diff --git a/plugins/data-designer-retrieval-sdg/pyproject.toml b/plugins/data-designer-retrieval-sdg/pyproject.toml
index bc57a50..a8b0680 100644
--- a/plugins/data-designer-retrieval-sdg/pyproject.toml
+++ b/plugins/data-designer-retrieval-sdg/pyproject.toml
@@ -27,6 +27,9 @@ classifiers = [
 embedding-dedup = "data_designer_retrieval_sdg.plugins:embedding_dedup_plugin"
 document-chunker = "data_designer_retrieval_sdg.plugins:document_chunker_plugin"
 
+[project.entry-points."data_designer.recipes"]
+retrieval-sdg = "data_designer_retrieval_sdg.recipe:recipe"
+
 [project.scripts]
 data-designer-retrieval-sdg = "data_designer_retrieval_sdg.cli:main"
 
diff --git a/plugins/data-designer-retrieval-sdg/src/data_designer_retrieval_sdg/recipe.py b/plugins/data-designer-retrieval-sdg/src/data_designer_retrieval_sdg/recipe.py
new file mode 100644
index 0000000..8540ff7
--- /dev/null
+++ b/plugins/data-designer-retrieval-sdg/src/data_designer_retrieval_sdg/recipe.py
@@ -0,0 +1,158 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Data Designer recipe registration for the retrieval SDG pipeline."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any, Literal, cast
+
+from data_designer.config.config_builder import DataDesignerConfigBuilder
+from data_designer.recipes.recipe import DataDesignerRecipe
+from pydantic import BaseModel, Field, model_validator
+from typing_extensions import Self
+
+from data_designer_retrieval_sdg.pipeline import (
+    DEFAULT_CHAT_MODEL,
+    DEFAULT_EMBED_MODEL,
+    DEFAULT_PROVIDER,
+    build_qa_generation_pipeline,
+)
+from data_designer_retrieval_sdg.seed_source import DocumentChunkerSeedSource
+
+
+class RetrievalSdgRecipeConfig(BaseModel):
+    """Configuration for the installed ``retrieval-sdg`` recipe.
+
+    The recipe is intentionally a thin composition wrapper around the existing
+    package pipeline. It gives the main ``data-designer`` CLI a way to discover
+    and run the pipeline without adding package-specific subcommands to
+    DataDesigner itself.
+    """
+
+    input_dir: Path = Field(description="Directory containing input text files.")
+    generated_output_dir: Path | None = Field(
+        default=None,
+        description="Optional directory where the generated dataset is also exported as JSON after create.",
+    )
+    conversion_output_dir: Path | None = Field(
+        default=None,
+        description="Optional output directory for retriever-format conversion when corpus_id is set.",
+    )
+    corpus_id: str | None = Field(
+        default=None,
+        description="Optional corpus identifier. When set, postprocess also runs retriever data conversion.",
+    )
+
+    file_pattern: str = "*"
+    recursive: bool = True
+    file_extensions: list[str] | None = Field(default_factory=lambda: [".txt", ".md", ".text"])
+    min_text_length: int = Field(default=50, ge=0)
+    sentences_per_chunk: int = Field(default=5, ge=1)
+    num_sections: int = Field(default=1, ge=1)
+    num_files: int | None = Field(default=None, ge=1)
+
+    multi_doc: bool = False
+    bundle_size: int = Field(default=2, ge=1)
+    bundle_strategy: Literal["sequential", "doc_balanced", "interleaved"] = "sequential"
+    max_docs_per_bundle: int = Field(default=3, ge=1)
+    multi_doc_manifest: Path | None = None
+
+    start_index: int = Field(default=0, ge=0)
+    end_index: int = Field(default=199, ge=0)
+    max_artifacts_per_type: int = Field(default=2, ge=1)
+    num_pairs: int = Field(default=7, ge=1)
+    min_hops: int = Field(default=2, ge=1)
+    max_hops: int = Field(default=4, ge=1)
+    min_complexity: int = Field(default=4, ge=1)
+    similarity_threshold: float = Field(default=0.9, ge=0.0, le=1.0)
+    max_parallel_requests_for_gen: int | None = Field(default=None, ge=1)
+
+    artifact_extraction_model: str = DEFAULT_CHAT_MODEL
+    artifact_extraction_provider: str = DEFAULT_PROVIDER
+    qa_generation_model: str = DEFAULT_CHAT_MODEL
+    qa_generation_provider: str = DEFAULT_PROVIDER
+    quality_judge_model: str = DEFAULT_CHAT_MODEL
+    quality_judge_provider: str = DEFAULT_PROVIDER
+    embed_model: str = DEFAULT_EMBED_MODEL
+    embed_provider: str = DEFAULT_PROVIDER
+
+    @model_validator(mode="after")
+    def validate_index_range(self) -> Self:
+        """Validate the configured seed index range."""
+        if self.end_index < self.start_index:
+            raise ValueError("end_index must be greater than or equal to start_index")
+        return self
+
+
+def build_recipe_config(recipe_config: BaseModel) -> DataDesignerConfigBuilder:
+    """Build the retrieval SDG Data Designer config from a recipe config."""
+    config = cast(RetrievalSdgRecipeConfig, recipe_config)
+    seed_source = DocumentChunkerSeedSource(
+        path=str(config.input_dir),
+        file_pattern=config.file_pattern,
+        recursive=config.recursive,
+        file_extensions=config.file_extensions,
+        min_text_length=config.min_text_length,
+        sentences_per_chunk=config.sentences_per_chunk,
+        num_sections=config.num_sections,
+        num_files=config.num_files,
+        multi_doc=config.multi_doc,
+        bundle_size=config.bundle_size,
+        bundle_strategy=config.bundle_strategy,
+        max_docs_per_bundle=config.max_docs_per_bundle,
+        multi_doc_manifest=str(config.multi_doc_manifest) if config.multi_doc_manifest else None,
+    )
+    return build_qa_generation_pipeline(
+        seed_source=seed_source,
+        start_index=config.start_index,
+        end_index=config.end_index,
+        max_artifacts_per_type=config.max_artifacts_per_type,
+        num_pairs=config.num_pairs,
+        min_hops=config.min_hops,
+        max_hops=config.max_hops,
+        min_complexity=config.min_complexity,
+        similarity_threshold=config.similarity_threshold,
+        max_parallel_requests_for_gen=config.max_parallel_requests_for_gen,
+        artifact_extraction_model=config.artifact_extraction_model,
+        artifact_extraction_provider=config.artifact_extraction_provider,
+        qa_generation_model=config.qa_generation_model,
+        qa_generation_provider=config.qa_generation_provider,
+        quality_judge_model=config.quality_judge_model,
+        quality_judge_provider=config.quality_judge_provider,
+        embed_model=config.embed_model,
+        embed_provider=config.embed_provider,
+    )
+
+
+def postprocess_recipe_results(results: Any, recipe_config: BaseModel) -> None:
+    """Export generated recipe results and optionally run retriever conversion."""
+    config = cast(RetrievalSdgRecipeConfig, recipe_config)
+    if config.generated_output_dir is None:
+        return
+
+    config.generated_output_dir.mkdir(parents=True, exist_ok=True)
+    generated_path = config.generated_output_dir / "generated.json"
+    generated_df = results.load_dataset()
+    generated_df.to_json(generated_path, orient="records", indent=2)
+
+    if config.corpus_id is None:
+        return
+
+    from data_designer_retrieval_sdg.convert import run_conversion
+
+    run_conversion(
+        input_path=str(generated_path),
+        corpus_id=config.corpus_id,
+        output_dir=str(config.conversion_output_dir) if config.conversion_output_dir else None,
+    )
+
+
+recipe = DataDesignerRecipe(
+    name="retrieval-sdg",
+    description="Generate synthetic retriever QA data from text documents.",
+    config_model=RetrievalSdgRecipeConfig,
+    build_config=build_recipe_config,
+    postprocess=postprocess_recipe_results,
+)