One search. Connected ideas.

Vision models interpret image content, such as objects, layout, handwriting, and visual relationships. Image understanding produces descriptions or structured observations; it does not automatically verify authenticity or determine business liability. Quality…

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.

Priority key

Document AI combines text extraction, OCR, layout/table understanding, and field extraction. A PDF may already contain usable text or require image interpretation. OCR reads characters; document understanding connects them to fields and structure.

Speech-to-text converts spoken words into text. Text-to-speech produces spoken output from text. Audio understanding can also process nontext information in audio, depending on the model, but reliable interpretation of emotions or identity should not be…

A voice agent handles a live conversation, including listening, responding, interruption, and tools. Video understanding combines information from sampled frames, timestamps, and sometimes audio. Both involve timing and event coordination beyond an ordinary…

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…

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…

A checkpoint records execution progress and state at a recovery boundary. Persistence saves it outside the process. Memory is selected information reused by later turns or tasks; a checkpoint is not automatically good long-term user memory.

Precision measures how many retrieved items are relevant. Recall measures how much relevant evidence was retrieved. Relevance indicates usefulness for the query; ranking metrics also consider whether good evidence appears early enough to be used.

RAG evaluation measures retrieval quality and answer quality independently and together. Fine-tuning changes model behavior through training; long context supplies a large body of content directly; traditional search returns evidence without generating an…

Reranking is a second ranking stage that examines a short candidate list more carefully. A reranker often reads the query and each candidate together, allowing richer comparison than separate embeddings. It changes candidate order; it cannot recover a…

Vision models interpret image content, such as objects, layout, handwriting, and visual relationships. Image understanding produces descriptions or structured observations; it does not automatically verify authenticity or determine business liability. Quality…

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.

Priority key

Document AI combines text extraction, OCR, layout/table understanding, and field extraction. A PDF may already contain usable text or require image interpretation. OCR reads characters; document understanding connects them to fields and structure.

Speech-to-text converts spoken words into text. Text-to-speech produces spoken output from text. Audio understanding can also process nontext information in audio, depending on the model, but reliable interpretation of emotions or identity should not be…

A voice agent handles a live conversation, including listening, responding, interruption, and tools. Video understanding combines information from sampled frames, timestamps, and sometimes audio. Both involve timing and event coordination beyond an ordinary…

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…

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…

Precision measures how many retrieved items are relevant. Recall measures how much relevant evidence was retrieved. Relevance indicates usefulness for the query; ranking metrics also consider whether good evidence appears early enough to be used.

A checkpoint records execution progress and state at a recovery boundary. Persistence saves it outside the process. Memory is selected information reused by later turns or tasks; a checkpoint is not automatically good long-term user memory.

A clear task, trusted constraints, explicitly separated input, expected output, and a defined response when information is missing. For policy answers, I require supporting evidence and forbid inventing unsupported policy conditions.

An LLM processes language using learned patterns and supplied context. I use it to summarize, extract, classify, explain, and propose tool actions. Current claim status and business permissions come from services, not the model's memory.

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