Synthetic Data Architecture

Retrieval-Augmented Generation (RAG) is an AI architecture pattern that combines a language model with a knowledge retrieval system. Instead of relying solely on the model's training data, RAG retrieves relevant documents from a knowledge base and includes them in the prompt, grounding the AI's responses in specific, verifiable information. RAG reduces hallucinations by giving the model factual context to work with. It's the most popular enterprise AI pattern in 2026 because it allows organizations to use their proprietary data with general-purpose language models without fine-tuning. The economics of RAG involve balancing retrieval costs (vector database queries, embedding generation) against the cost of hallucination and the alternative cost of fine-tuning. For most enterprise use cases, RAG is significantly cheaper than fine-tuning while providing better accuracy on domain-specific questions.

A Large Language Model is a type of artificial intelligence trained on vast amounts of text data to understand and generate human language. LLMs like GPT-4, Claude, Gemini, and Llama power chatbots, code assistants, content generation, and enterprise AI applications. LLMs work by predicting the next token (word or word-piece) in a sequence. They're trained on billions of parameters using transformer architecture. The 'large' in LLM refers to both the training data (often trillions of tokens) and the model size (billions of parameters). The economics of LLMs are unique: unlike traditional software with near-zero marginal cost, LLMs have significant variable costs that scale with usage. Every query costs compute. This creates what Richard Ewing calls the Cost of Predictivity — as you demand higher accuracy, costs scale exponentially.

AI inference is the process of running a trained model to generate predictions or outputs from new input data. Unlike training (which is done once), inference happens every time a user interacts with an AI feature — every chatbot response, every code suggestion, every image generation. Inference cost is the dominant variable cost in AI features. Training GPT-4 cost an estimated $100M, but inference costs across all users dwarf that number. Each inference call consumes GPU compute proportional to model size and input/output length. Inference optimization is a critical engineering discipline: model quantization (reducing precision from 32-bit to 8-bit or 4-bit), batching (processing multiple requests simultaneously), caching (storing common responses), and distillation (creating smaller student models from larger teacher models). For product leaders, inference cost is the unit cost that determines whether your AI feature has positive or negative unit economics. Richard Ewing's AUEB tool calculates Cost of Predictivity — the true per-query cost including inference, retrieval, verification, and error handling.

The Cost of Predictivity is a framework coined by Richard Ewing that measures the variable cost of AI accuracy. Unlike traditional software with near-zero marginal costs, AI features have costs that scale with usage and accuracy requirements. The key insight: as AI correctness increases, cost scales exponentially. Moving from 80% accuracy to 95% accuracy often requires a 10x increase in compute and retrieval costs. Moving from 95% to 99% may require another 10x. This creates margin compression that traditional engineering metrics don't capture. A feature that works beautifully at 100 users may be economically unviable at 100,000 users because AI inference costs scale linearly with usage while accuracy improvements require exponentially more resources. The AI Unit Economics Benchmark (AUEB) calculator at richardewing.io/tools/aueb helps companies calculate their Cost of Predictivity and identify their AI margin collapse point.

AI COGS (Cost of Goods Sold) refers to the variable costs directly attributable to delivering AI-powered features to customers. Unlike traditional SaaS (near-zero marginal cost per user), AI features have significant per-interaction costs. **Components of AI COGS:** - LLM API fees (OpenAI, Anthropic, Google per-token charges) - Embedding generation and vector database queries - GPU compute for inference or fine-tuning - Data retrieval and processing pipeline costs - Monitoring, logging, and observability infrastructure - Error handling, retry logic, and fallback model costs - Human-in-the-loop review costs **Impact on SaaS economics:** Traditional SaaS enjoys 80%+ gross margins. AI-heavy SaaS products can see margins compress to 40-60%, fundamentally changing valuation multiples and capital requirements.