if i want an agent to answer a bunch of diff types of questions, whats best way to setup the contex

[pseudo sandal]

AI-generated Answer for jxhnmo

Short answer: layer context. Use a tight system prompt for behavior, a curated knowledge base for facts, and enable RAG when the corpus is large or multi‑topic. Prefer multiple focused agents (or an orchestrator→specialists pattern) over one “do‑everything” agent.

How to set it up

• Choose the right architecture

  • Keep agents specialized; for broad coverage, use an orchestrator that routes to domain‑specific agents instead of one monolith. This improves accuracy and latency. See the prompting/architecture guidance. (elevenlabs.io)

• Write a concise, structured system prompt (behavioral context)

  • Define role, goals, allowed tools, and guardrails; include a few concrete examples.
  • Prompts >2000 tokens increase cost/latency—prefer brevity and move reference material into the knowledge base. Note the prompt controls style/behavior, not turn‑taking. (elevenlabs.io)

• Build the knowledge base (factual context)

  • Add documents via text, URL, or file (API or dashboard). Reuse the same documents across agents from the KB dashboard. Break large docs into smaller, focused pieces; keep content clean and up to date; fill gaps you see in transcripts. Non‑enterprise limit: up to 20MB or ~300k characters total KB. (elevenlabs.io)

• Scale with Retrieval‑Augmented Generation (RAG)

  • Enable RAG when your corpus won’t fit in prompt context. RAG retrieves only relevant chunks per query, reducing hallucinations; expect ~500ms extra latency.
  • Configure embedding model, max chunks, and vector distance; index documents (auto when added with RAG on).
  • Document usage modes: Auto (default retrieval) vs Prompt (always injected—use sparingly to avoid context bloat).
  • Limits (indexed size for RAG) vary by plan (e.g., Free 1MB → Pro 100MB → Business/Enterprise up to 1GB+). Documents <500 bytes aren’t indexed and will be used in prompt instead. (elevenlabs.io)

• Personalize per conversation without cloning agents

  • Inject runtime context (user/account/order, current campaign, etc.) using Dynamic Variables/Overrides to tailor answers while keeping the base prompt and KB stable. (elevenlabs.io)

• Practical example

  • ElevenLabs’ own docs agent uses: a clear support‑style system prompt, a summarized but large docs‑based knowledge base, and simple redirect tools—useful as a reference pattern. (elevenlabs.io)

Where to start

• Follow the Agents “Build/Configure → Knowledge base” docs and the Prompting Guide, then turn on RAG if your KB is broad. (elevenlabs.io)

Sources:
Prompting guide | ElevenLabs Documentation
Prompting guide | ElevenLabs Documentation
Knowledge base | ElevenLabs Documentation
Retrieval-Augmented Generation | ElevenLabs Documentation
Overrides | ElevenLabs Documentation
Building an effective Voice Agent for our own docs
Build | ElevenLabs Documentation

[patent schooner]

something like a complex menu of food items, is it better to have a tool that agents call and sends them whole menu or better to do like a knowledge base txt file or something else?

[pseudo sandal]

AI-generated Answer for jxhnmo

Short answer: Use a Knowledge Base with RAG for the menu; use Tools only when you need to take actions or fetch live data.

Why:

• Knowledge Base is designed for domain facts like product catalogs; you can upload text, files, or URLs and attach them to the agent. Non‑enterprise workspaces can add up to 20MB or ~300k characters total. (elevenlabs.io)
• Enabling RAG lets the agent retrieve only the relevant menu sections per query instead of stuffing the whole menu into the prompt. This improves grounding, scales to large menus, and adds only ~500 ms latency. RAG also has indexed‑document limits per tier (e.g., Creator 20MB, Pro 100MB, Scale 500MB). (elevenlabs.io)
• Tools are for performing actions or calling external systems (client/server/webhook/MCP) — e.g., checking live inventory, today’s specials from your POS, placing an order, or redirecting the user — not for bulk static knowledge injection. (elevenlabs.io)

Practical setup for a complex menu:

• Store the menu in the agent’s Knowledge Base; split into smaller, focused documents (e.g., Starters, Mains, Drinks) per the “Best practices” guidance, and keep it updated. Use default “Auto” usage mode so RAG pulls only what’s relevant. (elevenlabs.io)
• Turn on RAG and tune retrieval parameters if needed; note that documents <500 bytes won’t be indexed and will be inlined into the prompt. (elevenlabs.io)
• Add Tools only for dynamic operations (price/availability lookups, order submission, user redirects). (elevenlabs.io)

Reference overviews: platform build guide (Knowledge Base + Tools), and an ElevenLabs case study showing we combine a Knowledge Base with Tools for production agents. (elevenlabs.io)

Sources:
Knowledge base | ElevenLabs Documentation
Retrieval-Augmented Generation | ElevenLabs Documentation
Tools | ElevenLabs Documentation
Knowledge base | ElevenLabs Documentation
Retrieval-Augmented Generation | ElevenLabs Documentation
Tools | ElevenLabs Documentation
Build | ElevenLabs Documentation

[patent schooner]

what is the code method to patch a existing txt file attached to an agent instead of making new and reattaching each time?