Stop AI Hallucinations in Production

Go From AI Janitor to AI Architect and build reliable, production-grade RAG systems.

Are you shipping RAG features while flying blind, waiting for a public failure? This course gives you the complete system to monitor, measure, and minimize hallucinations so you can build AI products with confidence.

What You Will Learn

Module Focus Outcome
1 Baseline & KPI Lock Learn to audit your existing RAG, find its failure points, and establish a concrete KPI for measuring hallucinations.
2 The Production-Grade Build Get hands-on with the code for robust retrieval, advanced prompt engineering, and building an automated evaluation harness.
3 Stress-Testing & Deployment Learn to load-test your system, create production-ready documentation, and confidently deploy your reliable RAG service.
  • Module 1: Baseline & KPI Lock
    Learn to audit your existing RAG, find its failure points, and establish a concrete KPI for measuring hallucinations.
  • Module 2: The Production-Grade Build
    Get hands-on with the code for robust retrieval, advanced prompt engineering, and building an automated evaluation harness.
  • Module 3: Stress-Testing & Deployment
    Learn to load-test your system, create production-ready documentation, and confidently deploy your reliable RAG service.

Who Is This Course For?

  • The Senior Engineer / Tech Lead

    You're tasked with building AI features, but you're frustrated with the unpredictability. This course gives you the systematic process to gain control, eliminate guesswork, and become the go-to AI expert on your team.

  • The CTO / Engineering Manager

    You need your team to ship reliable AI without causing a PR disaster. This course is the framework to de-risk your AI roadmap, establish best practices, and turn your team into a world-class AI engineering unit.

My Experience Building AI Systems:

  • Retrieval Augmented Generation for Customer Support

    Implemented the semantic search solution based on a vector database. Performed data analysis, including clustering and topic modeling, to find types of past support requests. Used GenAI to draft a suggested solution to new customer support requests.

  • AI-based reporting solution

    Worked on an AI-based solution for analyzing online reviews and comparing the performance of different branches of the same company.

What people say about me:

  • Martyna Urbanek-Trzeciak (Product Manager - Data Engineering)

    I worked with Bartosz while he was a member of Data Engineering team at Fandom. He is very professional and open to share his knowledge with his teammates and beyond. His approach was always very data-driven and he has great knowledge in Data Engineering area what made him very valuable partner in discussions.

  • Mariusz Kuriata (Senior Manager of Engineering - Head of Ops)

    It was my pleasure to work with Bartosz. Bartosz is a dedicated and experienced Data Engineer who showed a range of skills and readiness to help. I appreciated that I could count on Bartosz to lead sophisticated technical projects. Highly recommend!

  • Workshop participant

    I'm extremely impressed with Bartosz's expertise and experience. We covered all assignments, addressing various details, scenarios, and potential errors. Every question we asked was answered thoroughly. The workshop format of the sessions and small group activities were particularly enjoyable. We had opportunities to apply our new knowledge practically. The trainer remained accessible whenever questions arose. If any uncertainties emerged, the facilitator explained everything with patience."

Frequently Asked Questions:

  1. How much time does the course require?

    The course is self-paced, designed to be completed in 3-5 hours per week over 4 weeks. All you need is a block of focused time to watch the videos and, more importantly, apply the code and concepts to your own projects.

  2. Will I be able to apply this on my own after the course?

    Yes. The entire point of the course is to make you self-sufficient. You get all the code, templates, and runbooks. This isn't theory; it's a complete, repeatable system for building reliable AI.

  3. Will this work with my company's tech stack and AI models?

    The principles and code are designed to be platform-agnostic. The course covers how to create adapter layers for any LLM (OpenAI, Anthropic, open-source models) and how to integrate the evaluation harness with your existing infrastructure (AWS, GCP, Azure, etc.).

  4. How is this different from your previous consulting?

    My consulting work involved implementing these systems for high-paying clients. I created this course because I saw the same problems everywhere. This course productizes the entire system, giving you the exact same frameworks and tools for a fraction of the cost.