One search. Connected ideas.

Build from LLM fundamentals to RAG, LangGraph agents, secure workflows, and production AI systems.

Multi-hop RAG retrieves information in dependent steps: one finding determines the next search. Agentic RAG lets a bounded agent choose retrieval tools or decide whether another search is needed. A fixed two-step retrieval workflow can be multi-hop without…

Corrective RAG detects inadequate retrieval and applies a correction, such as reformulating search. Self-reflective RAG checks whether evidence or an answer is sufficient and supported. Context compression reduces retrieved text to the parts useful for the…

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…

RAG stands for retrieval-augmented generation. It retrieves relevant external information and provides that information to a model before asking for an answer. It connects language generation to a changing knowledge source without retraining the model.

Beginner — What is RAG?

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

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…

Hybrid RAG uses lexical and semantic retrieval together. Reranking carefully orders the retrieved candidates. Parent-child retrieval searches smaller child chunks but supplies a larger parent section when the child is selected.

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…

A RAG chatbot answers conversations using retrieved knowledge. The dialogue interface and retrieval pipeline are separate responsibilities.

Ask whether the answer depends on external knowledge and whether the corpus is too large or variable to supply directly. Use RAG for private/changing knowledge with source references; use exact tools for live records. If a handful of permitted documents are…

A conversational interface over an eligible knowledge corpus. It combines a document pipeline with a request-time answer pipeline.

Multi-hop RAG retrieves information in dependent steps: one finding determines the next search. Agentic RAG lets a bounded agent choose retrieval tools or decide whether another search is needed. A fixed two-step retrieval workflow can be multi-hop without…

Corrective RAG detects inadequate retrieval and applies a correction, such as reformulating search. Self-reflective RAG checks whether evidence or an answer is sufficient and supported. Context compression reduces retrieved text to the parts useful for the…

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…

RAG stands for retrieval-augmented generation. It retrieves relevant external information and provides that information to a model before asking for an answer. It connects language generation to a changing knowledge source without retraining the model.

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…

Beginner — What is RAG?

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

Hybrid RAG uses lexical and semantic retrieval together. Reranking carefully orders the retrieved candidates. Parent-child retrieval searches smaller child chunks but supplies a larger parent section when the child is selected.

RAG retrieves relevant external evidence and gives it to a model before answering. It lets the insurance assistant use current private policy documents and produce source-backed explanations instead of relying only on learned knowledge.

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…

An insurance assistant helps authenticated customers understand documents, check claim status, prepare submissions, and reach staff. It combines RAG, exact service lookups, and bounded agent behavior. It does not become the authoritative policy or…

“Next.js provides the interface and Node authenticates, validates, and applies budgets. A bounded router selects policy RAG, exact claim lookup, guided intake, or staff escalation. RAG returns applicable policy evidence with citations; tools enforce…

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