Need Help Developing a Chatbot That Guides Users Through App Creation in a No-Code Platform

API
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1

I’m a beginner in AI, so please correct me if I’m wrong or missing something obvious. I’m trying to learn and would really appreciate your help.

I’m building a chatbot for my SaaS low-code/no-code platform where users can design applications using drag-and-drop tools and custom configurations. Currently, I use a Retrieval-Augmented Generation (RAG) approach to let the bot answer “how-to” and “what-is” style questions, which works for general documentation and feature explanations.

However, the core challenge is this:
My users are developing applications inside the platform—for example, creating a Hospital Patient Management app. These use cases require domain-specific logic, like which fields to include, what workflows to design, what triggers to set, etc. These are not static answers but involve reasoning based on both platform capabilities and the app’s domain.

I’ve considered fine-tuning, but that adjusts existing model weights rather than adding truly new domain knowledge or logic. So fine-tuning alone doesn’t solve the problem.

What I really need is a solution where the chatbot can help users design apps contextually based on:

  • What kind of app they want to create (e.g., patient management,
    inventory, CRM)

  • The available tools in the platform (Forms, Workflows, Datasets,
    Reports, etc.)

  • Logical reasoning to generate recommendations, field structures, and
    flows

What I’ve tried:

  • RAG with embedded documentation and examples

  • Fine-tuning with custom Q&A based on features

But still facing issues:

  • Lack of reasoning or “logical build” ability from the bot

  • No way to generalize across custom app types or domains

  • Chatbot can’t make recommendations like “Add these fields for patient
    management,” “Use this workflow for appointment scheduling,” etc.

Any help, architecture suggestions, or examples would be appreciated.

2

I’m impressed you were able to get that far without finetuning!

You can always start with a small number of examples and see if this has any impact on model accuracy. It may also help to employ the “reasoning before output” approach (or use o3/4/mini).

AI is just as much of an art as it is science. You don’t know what works until you try. Good luck!

EDIT: I see you mentioned already trying finetuning. I’d just make sure that your examples have sufficient domain-specific examples too.

1 Like
3

Thanks for the guidance.

It may also help to employ the “reasoning before output”

I didn’t understand this, can u explain it please

4

Technically it’s called chain-of-thought. That’s just when you instruct the LLM to think aloud before answering. Or follow a specific structure that eventually leads it into its answer once all the pieces are put together.

ChatGPT helped me craft this example:

System / Developer

You are an expert AI assistant for a low-code/no-code SaaS platform. When answering, explicitly show your reasoning before giving recommendations. Use this internal structure:

  1. Understand the user’s goal – restate in your own words.
  2. Inventory relevant tools – list Forms, Workflows, Datasets, Reports, etc.
  3. Outline logical steps – break down how you arrive at your design.
  4. Recommend configuration – fields, workflows, triggers, and why.

Always start your answer with: “Let’s think this through step by step.”

User

I’m building a Hospital Patient Management app. I have these tools available:

  • Forms (to capture data)
  • Workflows (for automations)
  • Datasets (for storage)
  • Reports (for analytics)

Please help me design:

  • Which patient fields to include
  • What appointment-scheduling workflow to set up
  • Which triggers to configure for alerts
Assistant

Let’s think this through step by step.

1. Understand the user’s goal

You want to create a Hospital Patient Management app that:

  • Captures comprehensive patient information,
  • Lets staff schedule patient appointments,
  • Automates reminders and sends alerts for critical events.

2. Inventory relevant tools

You have:

  • Forms – to collect patient details and appointment requests.
  • Workflows – to automate actions (like confirmations, reminders).
  • Datasets – to securely store patient, appointment, and medical records.
  • Reports – for analytics (e.g., patient load, no-shows).

3. Outline logical steps

a. Patient Fields

  • Identify essential information for patient registration, clinical care, and communications.

b. Appointment-Scheduling Workflow

  • Capture appointment requests via a form, validate availability, confirm with patient and staff, and create reminders.

c. Alerts & Triggers

  • Set up event-driven actions for key steps—new patient registered, upcoming appointment, critical lab results, missed appointments, etc.

4. Recommend configuration

A. Fields to include for a Patient

In your Patient Dataset and Patient Registration Form, use the following fields:

  1. Demographics

    • Patient ID (auto-generated, unique)
    • First Name
    • Last Name
    • Date of Birth
    • Gender
    • Contact Number
    • Email
    • Home Address

  2. Medical Information

    • Primary Physician
    • Allergies
    • Chronic Conditions
    • Current Medications
    • Emergency Contact Name
    • Emergency Contact Phone

  3. Administrative

    • Insurance Provider
    • Insurance Policy Number
    • Date of Registration

(Optional: Secondary contact, preferred language, etc. based on your needs)

B. Appointment-Scheduling Workflow

Build this flow:

  1. Form: “Schedule Appointment” Form — patient or staff enters:

    • Patient (Lookup/Dropdown from Patient Dataset)
    • Appointment Type (Visit, Telemedicine, etc.)
    • Preferred Date/Time
    • Preferred Doctor/Provider
    • Notes/Reason for Visit

  2. Workflow:

    • Step 1: Validate availability
      • Check doctor’s schedule in the Appointments Dataset.
      • If slot is available, proceed; if not, prompt for alternative time.
    • Step 2: Create appointment record
      • Add entry to the Appointments Dataset.
    • Step 3: Confirmation
      • Send confirmation email/SMS to patient and provider.
    • Step 4: Reminders
      • Schedule automated reminder (email/SMS) 24 hours and 1 hour before the appointment.

Why:

  • Ensures double-booking is avoided.
  • Automates patient and provider communications.
  • Reduces no-shows with reminders.

C. Important Triggers and Alerts

Configure these automated triggers:

Event Trigger Alert/Action
New patient registered On patient record creation Welcome email/SMS to patient, notify admin
Upcoming appointment 24 hours / 1 hour before appointment (scheduled trigger) Send reminder email/SMS to patient and doctor
Appointment missed/no-show Status not updated to “Checked In” within appointment window Notify assigned doctor/admin
Critical lab result entry New critical result in patient record Immediate alert to assigned physician and admin
Appointment rescheduled/cancelled Appointment status changed Notify patient and relevant staff

(Modify/add to suit additional hospital processes!)

Summary Table

Feature Tool Key Fields / Actions
Patient Registration Form/Dataset ID, Name, DOB, Contact, Allergies, Emergency Contact, etc.
Appointment Scheduling Form/Workflow/Dataset Patient, Date/Time, Doctor, Reason
Reminders & Alerts Workflow Triggers on creation, time-based, or status change

Let me know if you need help with form layouts, workflow automation setup, or sample report ideas!

I used gpt-4.1 for this one. You don’t need to do this if you’re using a reasoning model like o3 or o4. But trying it out couldn’t hurt.

1 Like
5

The question is “what is your RAG with embedded documentation and examples”?

Provide an example of what your are prompting the LLM with in the single-shot or multi-shot context window, and what response you are receiving and “how it doesn’t meet your expectations”.

Otherwise, we can’t tell what your architecture and actual attempts have been so far, thus how could we advise on how to modify them?