How to build an Onboarding and Training AI Assistant
Helps users get clear answers to onboarding and training questions by referencing their uploaded documents and formatting the AI’s response for easy reading.
Challenge
Onboarding knowledge is often scattered across multiple documents, making it hard for new employees to find consistent, accurate answers without repeatedly asking HR or training teams.
Industry
Industrials
Department
IT
Integrations
OpenAI
TL;DR
Lets users ask onboarding or training questions.
Uses uploaded documents (manuals, guides, PDFs) as the single source of truth.
AI assistant provides clear, document-based answers.
Responses are formatted professionally before being shown to the user.
Reduces manual effort in answering repetitive onboarding and training questions.
Common Pain Points of Onboarding and Training
Employees struggle to find the right information in scattered onboarding materials.
Training teams spend too much time answering repetitive questions.
Inconsistent answers across departments create confusion.
Onboarding resources are often outdated or not centralized.
New hires waste time searching instead of focusing on learning.
What the Agent Delivers
Centralized, AI-powered assistant for onboarding and training.
Consistent answers pulled directly from company-provided documents.
Clear, structured responses that are easy to follow.
Ability to handle multiple document formats (PDF, DOCX, etc.).
Scalable support for new hires without adding workload to HR or training teams.
Step-by-Step Build (StackAI Nodes)
1. User Input (in-0)
Purpose: User types their onboarding or training question.
Role: Entry point for the query.
2. Files (doc-0)
Purpose: Upload onboarding/training documents (PDFs, Word docs, etc.).
Role: Extracts and processes text for the AI to reference.
3. OpenAI LLM (llm-0)
Purpose: Generates a document-informed answer.
Instructions to model:
“You are an Onboarding & Training Assistant. Answer user questions clearly, referencing the provided documents if possible. If you do not know the answer, politely say so.”
4. Template (template-0)
Purpose: Formats the AI’s answer into a professional markdown structure.
Role: Ensures clarity and readability.
5. Output (out-0)
Purpose: Displays the final formatted answer to the user.
Data Flow
User enters a question → User Input node captures it.
User uploads documents → Files node processes and extracts text.
AI assistant receives question + documents → generates answer.
Template node formats the output.
Output node displays the result.
