Bartosz Mikulski
AI Implementation Reliability Specialist | I help engineering leaders prevent $50,000+ in wasted AI projects that fail in production.
AI Implementation Reliability Expert
I help engineering leaders prevent $50,000+ in wasted AI implementations
Is your AI system at risk of production failure?
Take the free 10-minute assessment to identify critical vulnerabilities:
Start AI Readiness Assessment
The #1 Reason Your AI Implementation Fails
Most AI systems fail in production because of three critical evaluation gaps:
Synthetic Evaluation Datasets
Your team is testing with artificial data that doesn't represent real-world conditions
Pattern Recognition Gaps
Your engineers can't identify what's causing incorrect outputs
Missing Domain Expertise
Subject matter experts weren't involved in the development process
My 3-Step AI Implementation Success Path:
Step 1: AI Readiness Assessment
Identify your implementation vulnerabilities
FREE
Step 2: Implementation Scorecard Review
Get actionable recommendations
$400
Step 3: From Demo to Deployment: AI Implementation Framework
Transform your team's capabilities
By Application
What Engineers Say:
"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 training was exceptional because it successfully integrated theory with practical application. The exercises allowed us to immediately implement what we had learned, and the real project examples were incredibly useful."
"Everything was conducted excellently. A considerable amount of knowledge but presented gradually so that it was clear what led to what."
Recent Articles on AI Implementation Reliability:
How to Make AI Evaluation Affordable: Research-Backed Methods to Cut LLM Evaluation Costs
Why are your AI evaluation costs spiraling out of control, and what are the proven methods to reduce them without sacrificing quality?
The Hidden Reason Your RAG System Is Failing - The Problems Caused by Approximate Nearest Neighbor Search in Vector Databases
Discover why your RAG system might be failing due to Approximate Nearest Neighbor search limitations in vector databases. Learn how compute budgets affect search accuracy, why metadata filters complicate retrieval, and implement practical solutions to dramatically improve your RAG performance.
Why is it so hard to correctly estimate AI projects?
Why can't you estimate an AI project correctly and can you do anything about it?
Building Reliable AI: A Testing-First Approach
Learn how to properly test AI systems using familiar software testing concepts. Discover key metrics, alignment checks, and robustness testing strategies for reliable AI deployment.
From API Wrappers to Reliable AI: Essential MLOps Practices for LLM Applications
API wrapper or production-ready AI? Learn how proper LLMOps separates prototypes from reliable applications
Troubleshooting AI Agents: Advanced Data-Driven Techniques of Improving AI Agent Performance
Expert strategies for improving AI agent performance through better data retrieval, query generation, automated decision-making process, and response generation. The article covers data collection, metrics, and techniques to improve the agent's performance.
Comprehensive Guide to AI Workflow Design Patterns with PydanticAI code examples
Learn how to implement AI workflows and autonomous agents with PydanticAI. This guide shows an example implementation of patterns described in the Anthropic article 'Building effective agents' such as prompt chaining, routing, parallelization, and orchestrator-workers.
How Much Data Do You Need to Improve RAG Performance?
A data-driven approach for improving RAG performance. Learn how to gather data and how much data you need for RAG, fine-tuning LLM, and training a specialized LLM from scratch.
The Ultimate 2025 Guide to Prompt Engineering
Discover the difference between proven prompt engineering techniques and tricks