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:

  • Machine Learning Infrastructure Migration

    Leading the strategic migration of the ML training infrastructure to Oracle Cloud (OCI) to overcome critical scalability bottlenecks. The legacy training pipeline, reliant on Jenkins-orchestrated scripts and limited compute resources, necessitated data downsampling, effectively capping model performance. I am currently architecting the transition to a cloud-native environment designed to enable training on full-scale datasets. This initiative involves navigating complex platform constraints to build a robust, high-capacity compute layer that decouples model training from CI/CD tooling.

  • Document Intelligence Pipeline Rescue

    Stabilized a failing document intelligence pipeline by enforcing deterministic AI outputs. I was brought in to rescue a no-code data extraction workflow that was failing due to non-deterministic LLM outputs and fragile JSON parsing logic. I re-architected the inference layer, replacing the brittle "prompt engineering" components with a dedicated, containerized microservice utilizing BAML for strict schema enforcement and automatic retry logic. This intervention transformed a crashing prototype into a production-grade system with 100% structural correctness and 95% extraction accuracy in under 24 hours.

  • Retrieval Augmented Generation (RAG) for Customer Support

    Architected a novel RAG system to enhance customer support efficiency by automating solution discovery. My core innovation was engineering a data pipeline that performed targeted information extraction from unstructured emails, creating a high-signal knowledge base that dramatically improved search relevance. To validate system performance, I established a robust, metrics-driven evaluation framework. Retrieval quality was benchmarked using Mean Reciprocal Rank (MRR@3), while the generative component was assessed with a QA process I designed to measure "Faithfulness" and "Semantic Similarity" to source documents. This systematically mitigated model hallucination and ensured all AI-drafted solutions were factually grounded.

  • Voice of Customer Intelligence Platform

    Architected a "Voice of Customer" intelligence platform to automate quality assurance across a distributed franchise network. I identified that manual review analysis was unscalable and lacked the granularity to distinguish between location-specific operational failures and brand-wide product issues. I engineered a semantic analysis pipeline using Generative AI that mapped unstructured customer feedback onto a rigid, pre-defined taxonomy of operational KPIs. This architectural decision transformed qualitative text into quantitative data, enabling the generation of automated, single-page performance audits that pinpointed specific areas for improvement (e.g., hygiene vs. product quality) for individual franchisees.

  • Machine Learning Platform

    Led the critical migration of the platform's service mesh from a monolithic Envoy configuration to Istio, significantly de-risking the shared compute cluster by isolating service configurations. This move eliminated the risk of single-point-of-failure outages, ensuring high availability for multiple clients in the multi-tenant environment. Additionally, I optimized the core model build pipeline by architecting a dependency caching strategy, cutting build times by over 30% and directly improving developer velocity. Served as an engineering expert for the customer support team, diagnosing and resolving complex, mission-critical build and deployment failures during the on-call duty.

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.