105158759937627581876105158759937627581876
105158759937627581876
Aug 16, 2025

Q: Questions before buying.

I am interested in purchasing your Plan 5, but I need to ask a few questions before making a decision.

I want to create an advanced chatbot or an AI agent to serve my clients exclusively. A key requirement is that the bot validates users with a unique client code from a Google Sheets list. If the code is valid, it should welcome them by name and provide a complete guide to our services, assessing their knowledge to know if they should start from scratch. If the code is invalid, it must deny any information, state that the service is for clients only, and indicate our subscription plans.

Is this possible with Release0?

How is the information for training the bot handled?
I have 5 Google Docs, each over 200 pages.
Is it possible to train the agent with this volume of documentation while maintaining an optimal level without errors?

Founder Team
release0

release0

Aug 16, 2025

A: Hi @105158759937627581876, great questions. Short answer: yes, your flow is doable in Release0, and it’s straightforward.

1) Client-only access with Google Sheets (or DB)

You can validate users against a “client code” stored in Google Sheets, greet valid users by name, and block access for non-clients.

How it works (high level):
1. Capture inputs → ask for client code (and optionally email/name).
2. Lookup in Google Sheets → match client_code and fetch the row (e.g., name, plan, etc.).
Docs: https://docs.release0.com/editor/blocks/integrations/google-sheets
3. Branching logic
• If found → “Welcome, {{name}}!” → continue to private content.
• If not found → show “clients only” message + your subscription plans.
4. Knowledge assessment → ask a few targeted questions (forms/buttons), set a skill level variable, then route users to “start from scratch” or “advanced” tracks.

This same pattern also works with NocoDB or Supabase if you prefer a database.

2) “Training” the bot on your 5×200-page docs

Release0 uses BYOK (Bring Your Own Key) for the LLM (OpenAI, Anthropic, Groq, via OpenRouter, etc.). We don’t retrain models; instead we use retrieval-augmented generation (RAG) so the agent cites your content.

What this means for your docs:
• You can index large documents (hundreds of pages each).
• Enable source-only / grounded mode so the bot answers only from your materials and says “I don’t know” when content isn’t found (reduces hallucinations).
• Add a response template that includes links or document titles in answers.

Video: how to add a trained model/KB to your agent → https://youtu.be/a4TMzobl-m0

Quality tips (to keep answers sharp):
• Prefer clean PDFs/Docs with clear headings; split extremely long files into logical sections (e.g., per chapter/guide).
• Use consistent terminology across docs so retrieval hits are precise.
• Turn on “must cite source” / require KB grounding for critical answers.
• Add guardrails: if confidence is low, ask a clarifying question or hand off.

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