One search. Connected ideas.

AI and LLM fundamentals

Quantization represents model values with fewer bits to reduce memory and sometimes improve speed. GGUF is a model file format used by compatible local inference runtimes; it is not itself a model or a guarantee of quantization level. Local embeddings run an…

Ollama simplifies running supported models locally through a runtime and API. Hugging Face hosts models, datasets, and libraries; Transformers is its widely used library for loading and using supported model architectures. A model repository is a distribution…

Local or self-hosted models run on infrastructure you control instead of a provider's shared inference API. An API model outsources much of inference operation. Self-hosting can mean a laptop, on-premises servers, or controlled cloud GPU infrastructure.

vLLM is an inference-serving system designed for efficient model execution. Serving techniques such as batching and attention-memory management help multiple requests share expensive hardware. GPU optimization is matching workload, memory, scheduling, and…

Priority key

Requirements: Expose reviewed customer lookup, order/policy lookup, ticket creation, and knowledge search to an approved host.

Pydantic validates data against Python model definitions. FastAPI uses declared routes and schemas to build HTTP services with validation and API documentation. Think of Pydantic as a role similar to Zod and FastAPI as a Python web framework with typed…

Beginner — What is FastAPI's role?

MCP is a protocol for connecting AI applications to external tools and context. The host is the application using the model; a client is its protocol connection to a server; a server exposes supported capabilities. The model normally proposes operations,…

Secure action flow: LLM proposal → allowlisted tool → identity/authorization → schema + rules → limits → business service → minimal result.

Graph RAG retrieves through entities and relationships, such as customer → policy → endorsement → covered asset. It can combine graph traversal, document retrieval, and generated summaries. A graph represents connected facts; it is separate from LangGraph's…

Quantization represents model values with fewer bits to reduce memory and sometimes improve speed. GGUF is a model file format used by compatible local inference runtimes; it is not itself a model or a guarantee of quantization level. Local embeddings run an…

AI and LLM fundamentals

Ollama simplifies running supported models locally through a runtime and API. Hugging Face hosts models, datasets, and libraries; Transformers is its widely used library for loading and using supported model architectures. A model repository is a distribution…

Local or self-hosted models run on infrastructure you control instead of a provider's shared inference API. An API model outsources much of inference operation. Self-hosting can mean a laptop, on-premises servers, or controlled cloud GPU infrastructure.

vLLM is an inference-serving system designed for efficient model execution. Serving techniques such as batching and attention-memory management help multiple requests share expensive hardware. GPU optimization is matching workload, memory, scheduling, and…

Only for a concrete Python library, local model, numerical workload, or scaling boundary. Otherwise another service adds authentication, deployments, networking, contracts, and failures without meaningful benefit.

Requirements: Expose reviewed customer lookup, order/policy lookup, ticket creation, and knowledge search to an approved host.

Pydantic validates data against Python model definitions. FastAPI uses declared routes and schemas to build HTTP services with validation and API documentation. Think of Pydantic as a role similar to Zod and FastAPI as a Python web framework with typed…

MCP is a protocol for connecting AI applications to external tools and context. The host is the application using the model; a client is its protocol connection to a server; a server exposes supported capabilities. The model normally proposes operations,…

Secure action flow: LLM proposal → allowlisted tool → identity/authorization → schema + rules → limits → business service → minimal result.

Graph RAG retrieves through entities and relationships, such as customer → policy → endorsement → covered asset. It can combine graph traversal, document retrieval, and generated summaries. A graph represents connected facts; it is separate from LangGraph's…

Priority key

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module