categorygrowinghigh complexity

Generative AI

Generative AI is a family of models that learn the statistical structure of data (text, images, audio, code, etc.) and then sample from that learned distribution to create new content. These models are typically built with deep neural architectures such as transformers, diffusion models, and GANs, and can be conditioned on prompts, examples, or structured inputs. In applications, generative models are often combined with retrieval systems, tools, and business logic to ground outputs in real data and workflows. Effective use requires careful attention to safety, reliability, governance, and alignment with domain constraints.

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Sub-patterns
End-to-End NNGenerative-ContentHybrid-Neuro-SymbolicRAG-StandardRAG-Graph
01

When to Use

  • When you need to generate natural language, code, or media that would be expensive or slow for humans to produce manually.
  • When tasks involve ambiguity, open-ended questions, or creative exploration rather than strict right-or-wrong answers.
  • When you can ground the model on reliable internal or external data sources to improve factuality.
  • When you want to augment human workers (e.g., drafting, summarizing, brainstorming) rather than fully automate decisions.
  • When existing deterministic or rule-based systems are brittle or too costly to maintain for complex language tasks.
02

When NOT to Use

  • When the task requires strict determinism, formal guarantees, or regulatory-grade traceability (e.g., certain financial calculations, safety-critical control systems) without a robust verification layer.
  • When you lack access to high-quality, authoritative data or subject-matter expertise to validate outputs in a high-risk domain.
  • When a simple rules engine, search, or template-based system can solve the problem more reliably and cheaply.
  • When user data is highly sensitive or regulated and you cannot meet compliance requirements with available model providers.
  • When you cannot allocate resources for ongoing monitoring, evaluation, and iteration after initial deployment.
03

Key Components

  • Foundation model (LLM, image generator, diffusion model, etc.)
  • Prompting and conditioning layer (prompts, templates, control signals)
  • Retrieval and context layer (RAG, search, vector databases)
  • Tooling and orchestration (agents, workflows, function calling)
  • Data pipelines for training, fine-tuning, and evaluation
  • Safety and governance layer (filters, guardrails, policy enforcement)
  • Monitoring and observability (telemetry, feedback loops, analytics)
  • Deployment infrastructure (APIs, GPUs/TPUs, scaling, caching)
  • Access control and security (auth, rate limiting, data isolation)
  • Evaluation and testing framework (automatic and human-in-the-loop)
Common Tools
OpenAIAnthropic ClaudeGoogle GeminiMeta LlamaMistral AIHugging Face TransformersLangChainLlamaIndexMicrosoft Azure AI StudioAmazon BedrockVertex AIPineconeWeaviateQdrantMilvus
Top Industries
04

Best Practices

  • Start with a clear business problem and measurable success metrics before selecting models or tools.
  • Use retrieval-augmented generation (RAG) or other grounding techniques to tie outputs to authoritative data sources.
  • Design prompts and system instructions explicitly, version them, and treat them as first-class configuration artifacts.
  • Implement multi-layer safety: input validation, output filtering, policy checks, and abuse detection.
  • Prefer API-based foundation models initially; only move to self-hosting when you have clear cost, control, or compliance drivers.
05

Common Pitfalls

  • Treating generative AI as a drop-in replacement for deterministic software without accounting for probabilistic behavior.
  • Relying on model outputs as ground truth without grounding, verification, or domain checks, leading to harmful hallucinations.
  • Skipping problem discovery and jumping straight to model integration, resulting in flashy demos but no business value.
  • Overfitting the solution to a single vendor’s API, making future migration or multi-vendor strategies difficult.
  • Sending sensitive or regulated data to external APIs without proper legal, security, and privacy review.
06

Learning Resources

tutorialGenerative AI Design Patterns: A Comprehensive GuidetutorialGenerative AI Design Patterns (GitHub catalog)tutorialGenerative AI Integration Patterns (Packt Publishing)tutorialAI Strategy Field Guide (Pattern-first, vendor-neutral)courseGenerative AI for Beginners (.NET) by MicrosoftpaperAttention Is All You NeedpaperDenoising Diffusion Probabilistic ModelstutorialOpenAI Cookbook: Examples and Guides for Working with LLMs
07

Example Use Cases

01Customer support assistant that drafts responses using company knowledge base via RAG.
02Marketing copy generator that produces personalized email campaigns and ad variants.
03Code assistant that suggests implementations, refactors, and tests within an IDE.
04Document summarization service for long reports, legal contracts, or research papers.
05Product search experience that answers natural language queries with grounded explanations.
08

Solutions Using Generative AI

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