> ## 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. # Get Generated Benchmark > Retrieve complete benchmark data with all samples. This endpoint returns the full benchmark dataset including all samples, their expected responses, and metadata. Use this to access the complete benchmark content for evaluation or review. **Path Parameters:** - `benchmark_id`: Unique benchmark identifier **Returns:** - `benchmark_id`: Benchmark identifier - `document_ids`: List of document IDs (single-element array for uploaded benchmarks, multiple elements for generated benchmarks) - `samples`: Full list of benchmark samples with their responses Identity, status, and timestamps are served by `GET /{benchmark_id}`. **Raises:** - `404`: If benchmark not found or user doesn't have access **Example Request:** ```json GET /api/v3/benchmarks/benchmark_01k4x9m2p7q3r6a1/data Headers: {"Authorization": "Bearer "} ``` **Example Response:** ```json { "benchmark_id": "benchmark_01k4x9m2p7q3r6a1", "document_ids": ["document_01k4x9m2p7q3r5se", "document_01k4x9m2p7q3r5sf"], "samples": [ { "sample_num": 1, "instruction": "What is the main purpose of the product?", "response": "To provide real-time collaboration tools for distributed teams." }, { "sample_num": 2, "instruction": "How does the authentication system work?", "response": "Uses OAuth 2.0 with JWT tokens for secure authentication." } ] } ``` **Notes:** - Only returns benchmarks owned by the user - Contains complete sample data for evaluation - Use this endpoint when you need the full benchmark content (not just metadata) ## OpenAPI ````yaml https://api.nugen.in/openapi-public.json get /api/v3/benchmarks/{benchmark_id}/data 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/{benchmark_id}/data: get: tags: - Benchmarks summary: Get Generated Benchmark description: >- Retrieve complete benchmark data with all samples. This endpoint returns the full benchmark dataset including all samples, their expected responses, and metadata. Use this to access the complete benchmark content for evaluation or review. **Path Parameters:** - `benchmark_id`: Unique benchmark identifier **Returns:** - `benchmark_id`: Benchmark identifier - `document_ids`: List of document IDs (single-element array for uploaded benchmarks, multiple elements for generated benchmarks) - `samples`: Full list of benchmark samples with their responses Identity, status, and timestamps are served by `GET /{benchmark_id}`. **Raises:** - `404`: If benchmark not found or user doesn't have access **Example Request:** ```json GET /api/v3/benchmarks/benchmark_01k4x9m2p7q3r6a1/data Headers: {"Authorization": "Bearer "} ``` **Example Response:** ```json { "benchmark_id": "benchmark_01k4x9m2p7q3r6a1", "document_ids": ["document_01k4x9m2p7q3r5se", "document_01k4x9m2p7q3r5sf"], "samples": [ { "sample_num": 1, "instruction": "What is the main purpose of the product?", "response": "To provide real-time collaboration tools for distributed teams." }, { "sample_num": 2, "instruction": "How does the authentication system work?", "response": "Uses OAuth 2.0 with JWT tokens for secure authentication." } ] } ``` **Notes:** - Only returns benchmarks owned by the user - Contains complete sample data for evaluation - Use this endpoint when you need the full benchmark content (not just metadata) operationId: benchmarks_data parameters: - name: benchmark_id in: path required: true schema: type: string title: Benchmark Id responses: '200': description: >- Returns the benchmark's samples. Identity, status, and timestamps are served by GET /{benchmark_id}. content: application/json: schema: $ref: '#/components/schemas/BenchmarkFullResponse' '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' security: - HTTPBearer: [] components: schemas: BenchmarkFullResponse: properties: benchmark_id: type: string title: Benchmark Id description: Benchmark ID examples: - benchmark_01k4x9m2p7q3r6a1 document_ids: items: type: string type: array title: Document Ids description: Document IDs used for generation examples: - - document_01k4x9m2p7q3r5s8 - document_01k4x9m2p7q3r5s9 samples: items: $ref: '#/components/schemas/BenchmarkSample' type: array title: Samples description: Complete list of benchmark samples examples: - - instruction: Summarise the indemnity cap. response: Liability is capped at twelve months of fees paid. sample_num: 1 metadata: anyOf: - additionalProperties: true type: object - type: 'null' title: Metadata description: Additional metadata error: anyOf: - type: string - type: 'null' title: Error description: Error message if data fetch failed type: object required: - benchmark_id title: BenchmarkFullResponse description: |- The benchmark's sample payload. Identity, status and timestamps are served by GET /{benchmark_id}. HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError BenchmarkSample: properties: sample_num: type: integer title: Sample Num description: Sample number instruction: type: string title: Instruction description: Instruction text response: type: string title: Response description: Expected response text image_url: anyOf: - type: string - type: 'null' title: Image Url description: Image URL for vision samples type: object required: - sample_num - instruction - response title: BenchmarkSample 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.