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.
The LLM ecosystem includes model providers, embedding technology, vector databases, APIs, and infrastructure enabling AI applications.
Large base models powering downstream tasks through APIs or local deployment.
Vector representations enabling semantic search, retrieval, and memory systems.
Unified interfaces for inference, tuning, and evaluation across providers.
Documents, knowledge bases, context.
Converted into vectors stored in a vector database.
LLM generates responses using retrieved context.
Chatbots, agents, analytics, automation.
Use embeddings + LLMs for knowledge retrieval.
Models coordinate APIs, memory, and tools.
Compare performance across providers and tasks.
No, embeddings are mostly needed for retrieval-based workflows.
Closed models are easier; open models offer more control. Many systems use hybrid strategies.
APIs require minimal setup; running your own models may require GPUs or cloud accelerators.
Start experimenting with APIs, embeddings, and models to unlock AI capabilities in your applications.
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