Are your teams ready to build GenAI assistants that deliver accurate, trusted answers every time?
GenAI-powered Q&A assistants are quickly becoming the new interface for knowledge, support, and decision-making. They bring your data to life by transforming complex content into clear, helpful answers to your users' most important questions.
The Challenge
Every organization wants internal copilots and knowledge assistants, but few teams know how to design them effectively. Most struggle with structuring data, managing retrieval, reducing hallucinations, and evaluating answer quality. Without a repeatable architecture and hands-on experience, efforts stay stuck in experimentation and never reach production.
Our Solution
In this hands-on workshop, your team will build a high-quality GenAI knowledge assistant step-by-step using curated notebooks, traces, and dashboards that mirror real workloads. Areas of focus include:
- Understanding the Users — Define user types, needs, and patterns that guide assistant behavior.
- Identifying and Ingesting Your Data — Select, prepare, and load the right source data to power accurate answers.
- Understanding User Requests — Interpret natural language questions and extract the information required to answer them.
- Searching For and Retrieving Required Data — Implement retrieval techniques to surface relevant context.
- Generating Personalized Responses — Build a pipeline that produces clear, accurate, and contextually grounded GenAI answers.
Skills You'll Gain:
- Reliable Q&A System Architecture — Design a clear, end-to-end blueprint for GenAI knowledge assistants that consistently deliver grounded answers.
- Production-Ready Assistant Pipelines — Build and validate a complete Q&A workflow from ingestion and retrieval through response generation.
- Improved Answer Accuracy & Fewer Hallucinations — Apply practical grounding and quality techniques that reduce unsupported or misleading responses.
- Reusable Retrieval & Prompting Patterns — Establish frameworks and workflows you can reuse across future Q&A and knowledge assistant use cases.
- Faster Delivery of New Knowledge Experiences — Use a repeatable build approach to launch, refine, and extend assistants as content and use cases evolve.
Who Should Attend:
Solution Essentials
Format
Virtual or in-person
Duration
2–3 hours
Skill Level
Basic Python recommended
Tools
- Jupyter notebooks plus preconfigured GenAI components