RAG

RAG, or retrieval-augmented generation, is a pattern where an agent retrieves relevant documents or data at query time and uses them to produce a more accurate, grounded response.

How RAG works

RAG stands for Retrieval-Augmented Generation. It lets an agent retrieve relevant information at query time and use that material to produce a more accurate answer.

In a real-time avatar system, RAG usually sits between the user's question and the model's response. The system searches a knowledge base, sends the best matching context to the model, and the avatar speaks the grounded answer.

A concrete example: a customer asks whether a product supports a specific integration. The avatar retrieves the integration doc, uses it to answer, and can point the user toward the correct setup path.

RAG is useful when the avatar needs current or private information that was not in the model's training data. The challenge is making retrieval fast enough that the conversation still feels live.

What Anam ships

Anam's Cara-4 model delivers expressive real-time avatars with around 150 ms server-side avatar-generation latency once a session is running, across 70+ languages. Builders use JavaScript and Python SDKs or integrations for LiveKit, Pipecat, ElevenLabs Agents, Agora, and VideoSDK. Bring any AI stack including OpenAI, Claude, Gemini, Mistral, Groq, Deepgram, Cartesia, or custom providers. The platform supports WebRTC delivery, SOC 2 Type II, HIPAA, zero data retention, and regional data residency. Sessions stream low-latency audio and video to browsers and native apps.

Explore the real-time avatar API →

Related terms

Vector database

A vector database stores embeddings for semantic search, so an avatar or agent can retrieve relevant knowledge base content for RAG and answer user questions with the right context.

Grounding

Grounding is the practice of anchoring a model's answer to trusted source content, retrieved documents, or tool results so an avatar can respond with verified information instead of guessing.

Hallucination

A hallucination is when a language model gives a confident but incorrect or unsupported answer, which is especially risky in avatar experiences because the response is delivered through a persuasive face and voice.

Function calling

Function calling lets an LLM invoke approved tools or APIs during a conversation, so an avatar can fetch data, perform actions, or answer from live systems instead of relying on text generation alone.

Frequently asked questions

What does RAG do for avatar agents?

RAG retrieves relevant source content before the model answers, so the avatar can respond from product docs, policies, support content, or other approved knowledge.

Why use RAG instead of only training the model?

RAG lets teams update knowledge without retraining a model. That matters for products, pricing, policies, and documentation that change faster than a model release cycle.

Does RAG slow down a real-time avatar?

It can if retrieval is slow or too broad. In real-time avatars, RAG has to be fast, selective, and integrated into the latency budget for the conversation.

What content should go into RAG for avatars?

Use content the avatar is allowed to rely on, such as product docs, help center articles, policy pages, pricing rules, API references, onboarding material, and approved sales notes.

Last updated: 17th July 2026 · Reviewed quarterly.

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