LLM Tech Stack & Model Ecosystem
Understanding APIs, foundation models, embeddings, open vs. closed models, and infrastructure choices.
Understanding APIs, foundation models, embeddings, open vs. closed models, and infrastructure choices.
Modern AI systems are built on layered components: APIs for interaction, models for reasoning, embeddings for semantic memory, and infrastructure to run everything efficiently. Understanding each layer helps teams build scalable and effective AI applications.
Interfaces to interact with LLMs, providing text generation, embeddings, reasoning, and more.
Large, pre-trained models like GPT, Llama, Claude, or open-source alternatives powering general intelligence tasks.
Numeric vector representations enabling search, retrieval, clustering, and semantic similarity.
Closed models offer convenience and strong performance, while open models provide customization, privacy, and local deployment.
Options include cloud APIs, self-hosted inference servers, quantized local runtimes, and distributed training clusters.
API calls, chat interfaces, agents, and tools.
Foundation models, fine-tuned models, adapters, and specialized reasoning models.
Vector databases, embedding models, retrieval pipes, and context optimization.
Documents, structured data, logs, knowledge bases.
Cloud GPU services, self-hosted inference servers, on-device acceleration, and orchestration tools.
Embeddings + vector search for intelligent document lookup.
Personalized or enterprise chatbots powered by foundation models.
Agents using APIs and models to perform tasks autonomously.
A large pre-trained model that can perform many general-purpose AI tasks.
Yes if your system requires search, memory, or semantic understanding of documents.
Closed models are easier and usually more powerful; open models provide flexibility and control.
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