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Fact beginner

Over 75% of Fortune 500 Use LLMs in Production

By early 2026, over 75% of Fortune 500 companies have deployed large language models in at least one production workflow, ranging from customer support chatbots to internal knowledge retrieval, code generation, and document processing pipelines.

enterpriseadoptionfortune-500production
Tip advanced

Optimal RAG Chunk Size Is 512-1024

For RAG applications, chunk your documents into 512-1024 token segments for optimal retrieval.

ragchunkingretrieval
Guide Advanced

Retrieval Augmented Generation (RAG)

RAG combines pre-trained LLMs with external knowledge retrieval. The LLM's knowledge is augmented by fetching relevant information from a private or dynamic dataset before generating a response, reducing hallucinations and improving factual accuracy.

Advanced LLMRAGKnowledge Retrieval
Tool

LlamaIndex

A data framework for connecting custom data sources to LLMs, providing tools for ingestion, structuring, retrieval, and query interfaces.

Explore Resource
ragdata sourcesindexing
Tutorial Advanced

Creating a Custom RAG System

Retrieval Augmented Generation (RAG) combines the knowledge of LLMs with specific document retrieval. To build a basic RAG system: 1) Process your documents into embeddings, 2) Store them in a vector database, 3) When queried, retrieve relevant content based on similarity, 4) Augment your prompt with this content, and 5) Generate contextually informed responses.

Estimated time: 30 minutes
ragvector databaseembeddings
Recommendation

LangChain Framework

A powerful framework for developing applications powered by language models, supporting document loading, prompt management, indexes for retrieval, chains, agents, and more.

Explore Resource
frameworkllm applicationsproduction
Tip Advanced

Consider Retrieval Augmented Generation (RAG)

For tasks requiring up-to-date or domain-specific knowledge, use RAG. Retrieve relevant information from a knowledge base first, then feed it to the LLM as context to generate the answer.

RAGknowledge basecontext
Recommendation

LangChain Framework

A powerful framework for developing applications powered by language models, supporting document loading, prompt management, indexes for retrieval, chains, agents, and more.

Explore Resource
frameworkllm applicationsproduction
Trivia

What does 'RAG' stand for in AI applications?

Q: What does 'RAG' stand for in AI applications?

A: Retrieval Augmented Generation.

RAGretrievalgeneration
Tutorial Advanced

Creating a Custom RAG System

Retrieval Augmented Generation (RAG) combines the knowledge of LLMs with specific document retrieval. To build a basic RAG system: 1) Process your documents into embeddings, 2) Store them in a vector database, 3) When queried, retrieve relevant content based on similarity, 4) Augment your prompt with this content, and 5) Generate contextually informed responses.

Estimated time: 30 minutes
ragvector databaseembeddings
Tool

LlamaIndex

A data framework for connecting custom data sources to LLMs, providing tools for ingestion, structuring, retrieval, and query interfaces.

Explore Resource
ragdata sourcesindexing

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