One search. Connected ideas.

Fine-tuning trains an existing model further using task-specific examples. It changes learned behavior rather than simply adding a document to the current prompt. It can target formatting, domain task performance, style, or consistent instruction following.

Dataset preparation converts representative examples into the selected training format with reviewed expected outputs. Evaluation compares trained behavior against the baseline on separate data. RAG supplies current external knowledge at runtime; fine-tuning…

PEFT means parameter-efficient fine-tuning: update a smaller subset of parameters instead of the whole model. LoRA trains small low-rank adapter matrices attached to selected model operations. QLoRA combines adapter training with a quantized frozen base model…

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…

AI and LLM fundamentals

Priority key

Ingestion: approved source → secure parse/OCR → coherent chunks + metadata → embeddings → complete published index.

Cost formula: input tokens × input rate + output tokens × output rate + tools/retrieval/embedding/infrastructure/review, with applicable caching and provider accounting. Prices must be checked for the real provider/model/account.

Fine-tuning trains an existing model further using task-specific examples. It changes learned behavior rather than simply adding a document to the current prompt. It can target formatting, domain task performance, style, or consistent instruction following.

Dataset preparation converts representative examples into the selected training format with reviewed expected outputs. Evaluation compares trained behavior against the baseline on separate data. RAG supplies current external knowledge at runtime; fine-tuning…

PEFT means parameter-efficient fine-tuning: update a smaller subset of parameters instead of the whole model. LoRA trains small low-rank adapter matrices attached to selected model operations. QLoRA combines adapter training with a quantized frozen base model…

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…

AI and LLM fundamentals

Priority key

Ingestion: approved source → secure parse/OCR → coherent chunks + metadata → embeddings → complete published index.

Cost formula: input tokens × input rate + output tokens × output rate + tools/retrieval/embedding/infrastructure/review, with applicable caching and provider accounting. Prices must be checked for the real provider/model/account.

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module