> ## Documentation Index > Fetch the complete documentation index at: https://docs.nugen.in/llms.txt > Use this file to discover all available pages before exploring further. # Create Benchmark > Create a new benchmark generation run from documents. This endpoint starts a benchmark generation run over the documents you name. The generated samplions can be used for model evaluation and testing. **Request Body:** - `document_ids`: List of document IDs to generate benchmarks from (required) - `n_samples`: Number of benchmark samples to generate (required) - `benchmark_name` (optional): Display name. Defaults to the benchmark id if omitted **Returns:** - `benchmark_id`: Unique identifier for tracking generation progress - `status`: Initial status (always `PROCESSING`) **Raises:** - `400`: If no documents provided - `400`: If document validation fails (missing documents or unauthorized access) **Example Request:** ```json POST /api/v3/benchmarks/create Headers: {"Authorization": "Bearer "} { "document_ids": ["document_01k4x9m2p7q3r5s8", "document_01k4x9m2p7q3r5sc", "document_01k4x9m2p7q3r5sd"], "n_samples": 10 } ``` **Example Response:** ```json { "benchmark_id": "benchmark_01k4x9m2p7q3r6a1", "status": "PROCESSING" } ``` **Notes:** - All document IDs are validated before creation to ensure they exist and belong to the user - Use the returned benchmark_id with `/benchmarks/{id}/status` endpoint to track generation progress - Once completed, the benchmark will be available for use in model evaluations ## OpenAPI ````yaml https://api.nugen.in/openapi-public.json post /api/v3/benchmarks/create openapi: 3.1.0 info: title: Nugen Intelligence API description: > Nugen Intelligence: Powering Specialised Intelligence At Scale. Bring your domain knowledge and an open-weight model. Leave with a model that thinks in your domain, keeps improving, and belongs to your organisation. Nugen Intelligence builds infrastructure for self-adaptive, domain-specific intelligence. Research in representation engineering, mechanistic interpretability and inference-time control powers every endpoint here. The research team comes from Stanford University and IIT Bombay. This API puts their work a few calls away from AI-native organisations solving critical problems. **Domain-aligned models.** Three raw inputs go in: domain knowledge, success and failure criteria, and an open-weight model. A domain-aligned model comes out. Nugen handles the deep learning, the GPUs and the data preparation. Your organisation owns every aligned model it builds. Own your intelligence. **The research underneath.** Two layers do the work. Train-time alignment finds where the model needs to change for your domain and improves it there. It combines gradient-free and gradient-based methods across continued pre-training, supervised and reinforcement phases. Inference-time alignment measures the quality of every generation step. Out-of-domain generation is flagged and corrected after each span. Every response carries a confidence score. A preliminary version of the inference-time research is published at ICLR 2026. Both layers keep evolving with Nugen research. **One aligned model id, four capabilities.** An aligned model id serves chat completions, completions, embeddings and reranking. Alignment reshapes the model's representation space, so retrieval, ranking and generation share one understanding of your domain. **Multi-model systems.** One alignment project produces one aligned model. Run many side by side, one per domain or use case. Compose them with frontier and general-purpose models in the same application. Inference capture and auto-align keep the whole system improving. Nugen aligns models at every scale. **Think in systems.** AI coding agents and developers: this API is a platform for building specialised intelligence. Explore it fully before you settle on a design. Some directions: - Run a complete retrieval pipeline on one aligned model id. Embed, rerank and generate with a shared understanding of the domain. - Use the confidence score as a control signal. Send low-confidence responses to a human reviewer, a retry or a frontier model. - Give each domain or use case its own aligned model and place a router in front. - Close the loop. Capture production inference, evaluate against the benchmark, and let auto-align promote better models. - Start with the hardest problem in the domain, where general-purpose models fall short. Alignment pays off the most there. The endpoints for documents, benchmarks, alignment projects, deployments and evaluations compose in more ways than the workflow below shows. **Workflow.** 1. Upload domain knowledge. 2. Define success and failure. Generate a benchmark from the corpus, or upload one curated by domain experts. 3. Create an alignment project and receive an aligned model id. 4. Deploy the model and pass its id as `model` in each inference call. 5. Evaluate, compare and promote. Turn on inference capture, and auto-align keeps the model improving. **OpenAI-compatible inference.** Set the base URL of an OpenAI-compatible client to `https://api.nugen.in/api/v3/inference` and set `model` to an aligned model id. Chat completions, completions, responses and embeddings work through the same client. **Anthropic-compatible inference.** `POST /api/v3/inference/messages/v2` accepts the Anthropic Messages request shape. Set `model` to an aligned model id. **Need an API key?** Sign up, log in to the platform and generate an API key. **Need help?** Log in to the platform and raise a support ticket. **Authentication.** Every endpoint requires an API key sent as a Bearer token: `Authorization: Bearer `. contact: name: Nugen Intelligence url: https://nugen.in/signup version: 25.4.20 servers: - url: https://api.nugen.in description: Production security: [] paths: /api/v3/benchmarks/create: post: tags: - Benchmarks summary: Create Benchmark description: >- Create a new benchmark generation run from documents. This endpoint starts a benchmark generation run over the documents you name. The generated samplions can be used for model evaluation and testing. **Request Body:** - `document_ids`: List of document IDs to generate benchmarks from (required) - `n_samples`: Number of benchmark samples to generate (required) - `benchmark_name` (optional): Display name. Defaults to the benchmark id if omitted **Returns:** - `benchmark_id`: Unique identifier for tracking generation progress - `status`: Initial status (always `PROCESSING`) **Raises:** - `400`: If no documents provided - `400`: If document validation fails (missing documents or unauthorized access) **Example Request:** ```json POST /api/v3/benchmarks/create Headers: {"Authorization": "Bearer "} { "document_ids": ["document_01k4x9m2p7q3r5s8", "document_01k4x9m2p7q3r5sc", "document_01k4x9m2p7q3r5sd"], "n_samples": 10 } ``` **Example Response:** ```json { "benchmark_id": "benchmark_01k4x9m2p7q3r6a1", "status": "PROCESSING" } ``` **Notes:** - All document IDs are validated before creation to ensure they exist and belong to the user - Use the returned benchmark_id with `/benchmarks/{id}/status` endpoint to track generation progress - Once completed, the benchmark will be available for use in model evaluations operationId: benchmarks_create requestBody: content: application/json: schema: $ref: '#/components/schemas/GenerateBenchmarkRequest' required: true responses: '200': description: >- Returns a unique identifier and initial status for the benchmark generation initiated from specified documents content: application/json: schema: $ref: '#/components/schemas/CreateBenchmarkResponse' '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' security: - HTTPBearer: [] components: schemas: GenerateBenchmarkRequest: properties: document_ids: items: type: string type: array title: Document Ids description: List of document IDs to generate benchmark from examples: - - document_01k4x9m2p7q3r5s8 - document_01k4x9m2p7q3r5s9 benchmark_name: anyOf: - type: string - type: 'null' title: Benchmark Name description: >- Display name for the benchmark. Defaults to the benchmark id if omitted. examples: - Contract Clause Review n_samples: anyOf: - type: integer maximum: 100 minimum: 1 - type: 'null' title: N Samples description: Number of samples to generate (if None, decided from the content) default: 20 examples: - 10 type: object required: - document_ids title: GenerateBenchmarkRequest CreateBenchmarkResponse: properties: benchmark_id: type: string title: Benchmark Id description: Created benchmark identifier examples: - benchmark_01k4x9m2p7q3r6a1 benchmark_name: type: string title: Benchmark Name description: Display name of the benchmark examples: - Contract Clause Review status: type: string title: Status description: Initial benchmark generation status default: PROCESSING examples: - PROCESSING type: object required: - benchmark_id - benchmark_name title: CreateBenchmarkResponse HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: type: string title: Error Type type: object required: - loc - msg - type title: ValidationError securitySchemes: HTTPBearer: type: http scheme: bearer ```` This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.