Supercharging AI with Context-Aware Models

[What is retrieval-augmented generation (RAG)?]

Large language models have finite training data and can hallucinate when they lack context.

RAG improves accuracy by retrieving relevant, authoritative data before the model responds.

[How RAG works]

A retrieval layer grounds the model before generation.

• User query is submitted
• Retrieval searches curated data for relevant information
• Retrieved data augments the prompt sent to the LLM
• The LLM generates an answer grounded in the retrieved context

[How RAG benefits businesses]

RAG aligns AI outputs with your policies, products, and brand instead of generic training data.

• Enhanced marketing assistants that reflect brand and product specifics
• Smarter contract generation grounded in your legal frameworks

[What is table-augmented generation (TAG)?]

TAG brings structured data into the flow, letting models reason over databases, spreadsheets, and records when numerical accuracy matters.

[How TAG works]

Structured retrieval powers precise, data-backed answers.

• User submits a request that requires structured data
• The system locates relevant tables from connected datasets
• Data is formatted and merged with the query for the model
• The model generates a response using both structured data and prior knowledge

[How TAG benefits businesses]

Structured retrieval keeps responses factual and current.

• Real-time financial reporting from live revenue and expense data
• Customer insights that use purchase history for personalized support
• Inventory updates with stock levels and reorder guidance from structured records

[What are embedding models?]

Embeddings convert text, images, or documents into vectors that capture semantic relationships so systems can search and compare ideas, not just words.

[How embedding models work]

Vector representations create semantic awareness for retrieval and ranking.

• Text or documents become high-dimensional vectors where similar meanings sit closer together
• Semantic search compares vectors instead of exact keywords
• Context awareness improves document retrieval, recommendations, and chat responses
• Example: business and company are close in meaning even though the words differ

[How embedding models benefit businesses]

Better semantic understanding improves every retrieval surface.

• Improved search that finds relevant content without exact terms
• Recommendation engines that match user intent to products or content
• Knowledge management that organizes and retrieves internal documents quickly