Software Engineer specialized in AI and ML
I'm Pavlo, a Software Engineer passionate about AI, ML, and building thoughtful digital products. This blog is my space to share what I'm learning, what I'm building, and the ideas worth exploring along the way.
Find me on LinkedIn and GitHub, or get in touch at ciao@pavlo.sh.
Projects
Articles
How to Integrate Artificial Intelligence into Business Processes Without Disrupting Everything
A practical, updated playbook for integrating AI into business processes in the LLM era. Learn how to assess readiness, pick high-impact use cases, decide between traditional ML and LLMs, evaluate systems before they ship, manage security risks like prompt injection, and roll out gradually without breaking operations.
June 13, 2026
Optimization of ML Models: Advanced Techniques to Reduce Resource Consumption
Models keep getting bigger while budgets do not. This updated guide covers the techniques that make machine learning and LLMs cheaper to run without sacrificing accuracy: quantization, pruning, knowledge distillation, feature selection, efficient hardware, and adaptive computation, with practical notes on applying them to large language models in production.
June 13, 2026
Embeddings Explained: Choosing the Right Model and Vector Database for Production
Your RAG system is only as good as the embeddings underneath it. This guide explains what embeddings actually are, how to choose an embedding model in 2026 without trusting leaderboards blindly, how dimensions affect cost and latency, and how to pick a vector database (Pinecone, Qdrant, Weaviate, Milvus, pgvector) based on scale, indexing, and filtering rather than hype.
June 7, 2026
Prompt Injection: The Security Hole in Every LLM App
Prompt injection is the number one security risk in LLM applications, and there is no patch that makes it go away. This guide explains direct and indirect injection, how data gets exfiltrated through tools and markdown images, the lethal trifecta that makes agents dangerous, and the defense-in-depth strategy that actually reduces your blast radius in production.
June 2, 2026
LLM Evaluation: Why Your Demo Works but Production Fails
Most LLM applications demo perfectly and then break with real users. This guide explains how to evaluate LLM applications properly: how to build an eval dataset, the metrics that actually matter, how to use LLM-as-a-judge without fooling yourself, and how to catch regressions before your users do.
May 30, 2026
Why Tokens Matter: The Hidden Unit That Shapes Your LLM Bills, Context, and Performance
Tokens are the fundamental unit of everything you do with LLMs: pricing, context limits, latency, retrieval, even multilingual fairness. This article explains what tokens really are, why they behave strangely across languages, and how a practical understanding of tokenization changes the way you design AI systems.
April 25, 2026
RAG in Production: What Nobody Tells You Before You Deploy
RAG sounds simple in theory: retrieve relevant chunks, inject them into the prompt, get better answers. In production, the reality is far messier. This guide covers the real failure modes, including chunking pitfalls, embedding drift, retrieval quality collapse, and latency traps, and what actually works to fix them.
April 21, 2026
LLM Context Window Limitations: Why More Tokens Hurt Your AI App Performance
Large language models advertise million-token context windows, but longer inputs silently degrade accuracy. Learn why the "lost in the middle" problem affects every major LLM, and what RAG and prompt structuring strategies actually work for production AI systems.
April 16, 2026
Agent Skills vs Multi-Agent Systems: Are We Witnessing the Next Architectural Shift in AI?
Agent Skills introduce a new paradigm for building AI systems by packaging operational knowledge into reusable modules. But can they truly replace multi-agent architectures? This article explores the trade-offs, strengths, and future of both approaches.
March 17, 2026
OpenAI Agents SDK: How to Build Agentic AI Applications in Python Easily
Learn how to build powerful, customizable agentic AI applications in Python using the OpenAI Agents SDK. Discover multi-agent orchestration, guardrails, and built-in tracing for production-ready AI workflows.
May 31, 2025