5. Generative AI Reference Architecture - stanlypoc/AIRA GitHub Wiki

Generative AI Reference Architecture

1. Introduction

1.1 Purpose

Framework for deploying generative AI systems across maturity levels (Pre-trained → Fine-tuned → Autonomous)

1.2 Audience

  • AI/ML Engineers
  • Solution Architects
  • Content Safety Teams
  • Legal/Compliance

1.3 Scope & Applicability

In Scope:

  • Text/Image/Video generation
  • Retrieval-Augmented Generation (RAG)
  • Multi-agent creative systems

Out of Scope:

  • Non-generative AI models
  • Hardware-specific optimizations

1.4 Assumptions & Constraints

Prerequisites:

  • Python 3.10+
  • CUDA 12.1+ for local deployments

Technical Constraints:

  • Minimum 16GB VRAM for SDXL models
  • <2s latency for interactive generation

Ethical Boundaries:

  • Watermarking required for synthetic media
  • No NSFW generation without consent layers

1.6 Example Models

Level Text Image Video
Level 2 GPT-3.5 Stable Diffusion 2.1 Zeroscope
Level 3 Llama 2 (fine-tuned) SDXL + ControlNet AnimateDiff
Level 4 AutoGPT + RAG Multi-agent Canvas Sora-like systems

2. Architectural Principles

Here’s a well-structured and practical set of Architecture Principles for Generative AI, tailored for building, scaling, and governing modern generative systems—from LLMs to diffusion models and beyond.

✨ 2.1 Architecture Principles for Generative AI

1. Prompt-Centric Design

Build for prompt flexibility and control, not just model fidelity.

  • Enable structured prompts, templates, and in-context learning patterns.
  • Support prompt chaining, history retention, and memory-based inputs.
  • Use embeddings, prompt caches, or tools like LangChain for orchestration.

2. Content Safety & Guardrails

Embed safety mechanisms into every layer of the stack.

  • Apply moderation filters (e.g., OpenAI Moderation, Perspective API).
  • Enforce policy-based outputs (e.g., avoid toxicity, bias, misinformation).
  • Enable human-in-the-loop review for sensitive use cases.

3. Multimodal Readiness

Design for text, image, code, audio, and video generation.

  • Use modular pipelines that support cross-modal prompting (e.g., text-to-image, audio-to-text).
  • Adopt unified input/output formats (e.g., JSON, base64, markdown).
  • Prepare infrastructure for GPU/TPU workloads per modality.

4. Fine-Tuning & Adaptability

Allow models to be customized safely and effectively.

  • Support instruction tuning, LoRA/PEFT, RAG (Retrieval-Augmented Generation).
  • Use adapters or domain-specific data for enterprise alignment.
  • Ensure reproducibility and dataset governance.

5. Retrieval-Augmented Generation (RAG) Integration

Augment generative models with factual grounding.

  • Integrate vector stores (e.g., Pinecone, Weaviate, FAISS).
  • Use context retrievers and hybrid search to inject enterprise knowledge.
  • Log what was retrieved, why, and when.

6. Latency & Cost Optimization

Generative models are resource-hungry—optimize wisely.

  • Use quantization, batching, and GPU inference optimization (e.g., vLLM, DeepSpeed).
  • Offload long-form generation to background tasks when applicable.
  • Cache embeddings, outputs, and model snapshots to minimize regeneration.

7. Output Control & Constraints

Define boundaries for generation using system rules.

  • Use token limits, stop sequences, regular expressions, or grammar constraints.
  • Leverage model settings (e.g., temperature, top_p, frequency_penalty) responsibly.
  • Implement feedback loops to penalize harmful, verbose, or off-topic outputs.

8. Observability & Feedback

Monitor model behavior, drift, and quality continuously.

  • Track metrics like BLEU, perplexity, human satisfaction ratings, prompt failure rates.
  • Log prompts and completions for audits and model refinement.
  • Enable real-time alerting for hallucinations or critical missteps.

9. Human-Centered Design

Generative AI should assist, not replace—design for co-creation.

  • Use copilots, assistants, and multi-turn dialog UX patterns.
  • Allow users to revise, undo, or guide generation interactively.
  • Respect user intent, tone, and constraints.

10. Responsible AI and Ethics

Ensure transparency, fairness, and accountability.

  • Clearly label AI-generated content.
  • Document training datasets, licensing, and fine-tuning sources.
  • Respect copyright, privacy, and compliance obligations.

11. Versioning & Governance

Treat models, prompts, and outputs as versioned artifacts.

  • Use model registries, prompt repositories, and changelogs.
  • Enable rollback, staging, and A/B testing.
  • Record decision logs, performance trends, and review outcomes.

12. Composability & Tool Use

Generative AI systems should interact with tools and APIs, not work in isolation.

  • Enable tool use through agent frameworks (e.g., ReAct, AutoGPT).
  • Allow external API calls, calculators, or code execution in workflows.
  • Use planners or orchestrators for task decomposition.

2.2 Standards Compliance

  1. Security & Privacy

    • Must comply with: C2PA for media provenance, PCI DSS for payment-related generation
    • Practical tip: Implement NVIDIA Picasso for content credentials

  2. Ethical AI

    • Key standards: Partnership on AI Synthetic Media Guidelines
    • Checklist item: Run outputs through Google SafeSearch API

2.3 Operational Mandates

5 Golden Rules:

  1. Always log prompt+seed combinations
  2. Triple-check training data copyright status
  3. Rate-limit by user trust level
  4. Human review for high-impact generations
  5. Real-time toxicity filtering

Sample Audit Log:

{
  "timestamp": "2023-11-20T14:23:12Z",
  "model": "stable-diffusion-xl-1.0",
  "prompt_hash": "sha3-512:9a8b...",
  "seed": 42,
  "safety_score": 0.92,
  "moderator_override": false
}

3. Architecture by Technology Level

3.1 Level 2 (Basic) - Pre-trained Model Consumption

Definition:
API-based consumption of foundation models

Key Traits:

  • No fine-tuning
  • Basic prompt engineering
  • Single modality

Logical Architecture:

graph LR
    A[User Prompt] --> B[API Gateway]
    B --> C[Pre-trained Model]
    C --> D[Safety Filter]
    D --> E[Output Delivery]

Cloud Implementations:

Provider Services Example Models
Azure OpenAI Service DALL-E 3, GPT-4
AWS Bedrock Claude 2, SDXL
GCP Vertex AI Imagen, PaLM 2

3.2 Level 3 (Advanced) - Fine-Tuned & RAG Systems

Definition:
Customized models with domain-specific knowledge

Key Traits:

  • LoRA adapters
  • Vector database integration
  • Multi-step generation

Logical Architecture:

graph LR
    A[Prompt] --> B[Query Planner]
    B --> C[Vector DB Lookup]
    C --> D[Augmented Generation]
    D --> E[Style Transfer]
    E --> F[Output Refinement]

Critical Components:

  • Embedding service (e.g., text-embedding-ada-002)
  • Adapter version control
  • Semantic cache

3.3 Level 4 (Autonomous) - Multi-Agent Systems

Definition:
Self-orchestrating creative agents

Key Traits:

  • Dynamic team formation
  • Cross-modal generation
  • Automated quality control

Logical Architecture:

graph LR
    A[Creative Brief] --> B[Director Agent]
    B --> C[Copywriter Agent]
    B --> D[Visual Agent]
    B --> E[Sound Agent]
    C & D & E --> F[Multimedia Assembler]
    F --> G[Quality Control Loop]

Safety Mechanisms:

  • Constitutional AI oversight
  • Style consistency enforcer
  • Copyright validator

4. Glossary & References

Terminology:

  • LoRA: Low-Rank Adaptation for efficient fine-tuning
  • Negative Prompting: Technique to exclude unwanted elements

References:

  1. C2PA Technical Specification
  2. NVIDIA Picasso Security Whitepaper