GenerativeAIExamples
ModelFreeGenerative AI reference workflows optimized for accelerated infrastructure and microservice architecture.
Capabilities12 decomposed
synthetic dataset generation via llm-based text synthesis with domain-specific templates
Medium confidenceNeMo Data Designer generates synthetic training datasets by combining LLM text generation with non-LLM samplers and domain-specific templates. The system uses a microservice architecture that accepts template definitions and sampling parameters, orchestrates LLM calls for content generation, and outputs structured datasets in multiple formats. Templates define the schema and generation logic, while samplers control diversity and distribution of generated examples.
Combines LLM-based generation with non-LLM samplers and domain-specific templates in a microservice, enabling reproducible synthetic data generation without manual annotation — differentiates from generic LLM APIs by providing structured template-driven generation with sampling control
Faster than manual data annotation and more controllable than raw LLM generation because templates enforce schema consistency and samplers control distribution, while self-hosted NIM deployment avoids cloud API costs at scale
continuous data flywheel with evaluation-driven refinement
Medium confidenceNeMo Data Flywheel implements a closed-loop system that generates synthetic data, evaluates model performance on that data, identifies failure modes, and automatically refines generation templates based on evaluation results. The system tracks metrics across iterations and uses evaluation feedback to adjust sampling parameters and template logic, creating a continuous improvement cycle without manual intervention.
Implements a closed-loop system where evaluation results automatically trigger template and sampler refinement without manual intervention — unique in combining synthetic data generation with automated evaluation feedback to create self-improving data pipelines
More efficient than manual data curation because it automates the identify-refine-validate cycle, and more principled than random data augmentation because refinements are driven by actual model performance metrics
safety and content moderation with guardrails and alignment evaluation
Medium confidenceNeMo Safe Synthesizer provides safety-focused data generation and evaluation by integrating content filtering, toxicity detection, and alignment checks into the data generation and evaluation pipelines. The system can generate synthetic data with safety constraints, evaluate model outputs for harmful content, and track safety metrics across model versions. Supports both rule-based filtering and LLM-based safety evaluation.
Integrates safety constraints into data generation and evaluation pipelines through NeMo Safe Synthesizer, enabling safety-aware synthetic data generation and alignment evaluation — differentiates from post-hoc safety filtering by building safety into the generation process
More effective than post-generation filtering because safety constraints are applied during generation, and more comprehensive than single-metric safety evaluation because it tracks multiple safety dimensions
framework-agnostic rag implementation with pluggable vector databases and embedding models
Medium confidenceProvides RAG reference implementations that abstract vector database and embedding model selection, allowing developers to swap implementations without changing application code. The system uses adapter patterns to support FAISS (in-memory), Milvus, Weaviate, Pinecone, and other vector databases, and supports multiple embedding models (NVIDIA NIM, OpenAI, HuggingFace). Configuration-driven setup enables rapid experimentation with different retrieval strategies.
Uses adapter patterns to support multiple vector databases and embedding models with configuration-driven setup, enabling RAG applications to switch implementations without code changes — differentiates from framework-specific RAG by providing true implementation portability
More flexible than framework-locked RAG because vector database and embedding model selection is decoupled from application logic, and more practical than manual integration because adapters handle API differences
retrieval-augmented generation (rag) pipeline orchestration across multiple frameworks
Medium confidenceProvides reference implementations of RAG pipelines supporting LangChain, LlamaIndex, and other frameworks, with pluggable components for embedding generation, vector storage, reranking, and LLM inference. The architecture decouples each RAG stage (retrieval, reranking, generation) as independent microservices, allowing developers to swap implementations (e.g., FAISS vs. Milvus for vector storage) without changing application code. Supports both cloud-hosted (NVIDIA API Catalog) and self-hosted (containerized NIM) inference patterns.
Decouples RAG stages (retrieval, reranking, generation) as independent microservices with pluggable implementations, enabling framework-agnostic RAG that supports both cloud-hosted and self-hosted inference patterns — differentiates from framework-specific RAG by providing portable, composable reference implementations
More flexible than framework-locked RAG because components are swappable, and more cost-effective than cloud-only RAG because self-hosted NIM deployment avoids per-query API costs while maintaining production-grade performance
multimodal rag with image and text retrieval fusion
Medium confidenceExtends RAG pipelines to handle multimodal documents containing both images and text by using separate embedding models for each modality and fusing retrieval results at the ranking stage. Images are embedded using vision models, text using language models, and a reranker scores cross-modal relevance to determine which documents (image or text) best answer the query. The system maintains separate vector indices for each modality and orchestrates cross-modal retrieval.
Fuses image and text retrieval by maintaining separate modality-specific embeddings and using cross-modal reranking to score relevance — unique in providing reference implementations for multimodal RAG that handle both modalities without requiring unified embedding spaces
More practical than single-modality RAG for technical documents because it retrieves both diagrams and explanatory text, and more efficient than naive cross-modal embedding because separate modality-specific models avoid representation bottlenecks
tool calling workflow with schema-based function registry and multi-provider support
Medium confidenceImplements structured tool calling by defining a schema-based function registry that maps tool definitions to LLM function-calling APIs across multiple providers (OpenAI, Anthropic, NVIDIA NIM). The system accepts tool schemas (name, description, parameters), orchestrates LLM calls with tool definitions, parses tool-use responses, and executes registered functions. Supports both native function-calling APIs and fallback parsing for models without native support.
Provides schema-based function registry with native support for OpenAI, Anthropic, and NVIDIA NIM function-calling APIs, enabling provider-agnostic tool definitions and execution — differentiates from provider-specific implementations by abstracting tool calling across multiple LLM backends
More portable than provider-locked tool calling because schemas are reusable across providers, and more reliable than string-based tool parsing because it uses native function-calling APIs with structured validation
embedding fine-tuning workflow with domain-specific optimization
Medium confidenceProvides end-to-end workflows for fine-tuning embedding models on domain-specific data using contrastive learning objectives. The system accepts training data with query-document pairs or triplets, orchestrates fine-tuning on NVIDIA GPUs using NeMo framework, and evaluates embeddings on domain-specific benchmarks. Supports both supervised fine-tuning (with labeled pairs) and unsupervised approaches (with hard negative mining).
Provides end-to-end fine-tuning workflows using NeMo framework with support for both supervised (labeled pairs) and unsupervised (hard negative mining) approaches, integrated with evaluation on domain-specific benchmarks — differentiates from generic fine-tuning by providing RAG-specific optimization and evaluation
More cost-effective than cloud embedding APIs for high-volume retrieval because fine-tuned embeddings can be deployed locally, and more effective than general embeddings because fine-tuning optimizes for domain-specific relevance
automated model evaluation with domain-specific metrics and benchmarking
Medium confidenceNeMo Evaluator provides automated evaluation of generative AI models using domain-specific metrics (accuracy, F1, BLEU, ROUGE, custom metrics) and benchmarking frameworks. The system accepts model outputs and ground truth labels, computes metrics in parallel, generates evaluation reports with statistical significance testing, and tracks metrics across model versions. Supports both task-specific metrics (e.g., code correctness for code generation) and general metrics (e.g., semantic similarity).
Provides automated evaluation with domain-specific metrics (code correctness, semantic similarity, task-specific metrics) and statistical significance testing integrated with the NeMo ecosystem — differentiates from generic evaluation by supporting task-specific metrics and tracking metrics across the data flywheel
More comprehensive than manual evaluation because it automates metric computation and statistical testing, and more actionable than single-metric evaluation because it provides detailed error analysis and failure mode identification
cloud-hosted inference via nvidia api catalog with zero-gpu setup
Medium confidenceProvides quick-start examples using NVIDIA API Catalog for LLM inference, embedding generation, and reranking without requiring local GPU infrastructure. Applications authenticate via API key and make REST calls to cloud-hosted models, enabling rapid prototyping and evaluation without infrastructure setup. Supports both synchronous and asynchronous API calls, with built-in retry logic and rate limiting.
Provides zero-GPU quick-start examples using NVIDIA API Catalog, enabling rapid prototyping without infrastructure setup — differentiates from self-hosted approaches by eliminating operational complexity at the cost of per-query API fees
Faster to prototype than self-hosted deployment because no GPU infrastructure setup is required, but more expensive at scale than self-hosted NIM deployment because API costs accumulate with volume
self-hosted inference with containerized nvidia nims and gpu orchestration
Medium confidenceProvides reference implementations for deploying NVIDIA NIM (NVIDIA Inference Microservices) containers on GPU infrastructure for LLM inference, embedding generation, and reranking. The system uses Docker Compose or Kubernetes for orchestration, manages GPU allocation and memory, and exposes OpenAI-compatible REST APIs. Supports multi-GPU inference with tensor parallelism and batching optimization for throughput.
Provides containerized NIM deployments with OpenAI-compatible APIs and multi-GPU orchestration using TensorRT optimization — differentiates from cloud-hosted inference by enabling on-premises deployment with full model control and cost optimization at scale
More cost-effective than API-based inference at high volume because infrastructure costs are amortized, and more compliant than cloud inference because data never leaves on-premises infrastructure
industry-specific solution templates for asset lifecycle management and sql integration
Medium confidenceProvides pre-built reference implementations for domain-specific applications including asset lifecycle management (tracking equipment, maintenance, depreciation) and SQL Server AI integration (semantic search over databases, natural language queries). These templates combine RAG, tool calling, and fine-tuned embeddings to solve industry problems without starting from scratch. Each template includes data schemas, evaluation benchmarks, and deployment guides.
Provides pre-built templates for asset lifecycle management and SQL semantic search that combine RAG, tool calling, and fine-tuned embeddings — differentiates from generic RAG by including domain-specific schemas, evaluation benchmarks, and deployment guides
Faster to deploy than building from scratch because templates include data schemas and evaluation benchmarks, and more effective than generic RAG because they optimize for domain-specific tasks
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
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Prompt-Engineering-Guide
🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
Prompt Engineering Guide
Comprehensive prompt engineering techniques and templates.
Prompt Engineering Guide
Guide and resources for prompt engineering.
deepeval
The LLM Evaluation Framework
Llama 3.3 70B
Meta's 70B open model matching 405B-class performance.
unsloth
Web UI for training and running open models like Gemma 4, Qwen3.5, DeepSeek, gpt-oss locally.
Best For
- ✓ML engineers building fine-tuning pipelines who need fast iteration on training data
- ✓Teams requiring domain-specific synthetic data (code, SQL, medical text) without manual labeling
- ✓Enterprises prototyping LLM applications before committing to data collection infrastructure
- ✓Teams building production LLM applications with continuous deployment cycles
- ✓Organizations needing automated data quality assurance without human-in-the-loop review
- ✓Projects where model performance directly drives data generation strategy
- ✓Organizations deploying LLMs in regulated industries (healthcare, finance, government)
- ✓Teams building customer-facing AI applications requiring safety guarantees
Known Limitations
- ⚠Generated data quality depends on LLM capability and template design — no automatic quality filtering
- ⚠Scaling to millions of examples requires careful cost management with cloud-hosted LLMs
- ⚠Domain-specific templates must be manually authored; no automatic template inference from examples
- ⚠Synthetic data may exhibit LLM biases and hallucinations without post-generation validation
- ⚠Requires well-defined evaluation metrics — garbage metrics lead to garbage data refinements
- ⚠Feedback loop latency can be high if evaluation is expensive or slow
Requirements
Input / Output
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Repository Details
Last commit: Mar 30, 2026
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Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.
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