Production LLM: Automated Code Review and Documentation – Part 2.
In our previous article, we looked at a supervisor-based architecture where a manager agent coordinated specialized agents for code review and documentation. Today’s solution takes this…
Production LLM: automated code review and documentation – Part 1.
In this article, we’ll explore how AI Agents can automate some everyday tasks in software development. Large Language Models (LLMs) are not just good at writing code—they can also help take…
Production LLM: Agent with memory and tools
In one of the previous articles, we created a RAG agent that used tools to yield pretty interesting results (Production LLM: Agent with tools). It could solve multiple tasks quite well:…
Production ML: Data Engineering Pipeline – E-commerce Example. Part 2.
In the previous article (Production ML: Data Engineering Pipeline – E-commerce Example. Part 1.), we’ve designed a solution to help us migrate our company data from the old Customer Data…
Production ML: Data Engineering Pipeline – E-commerce Example. Part 1.
A couple of days ago, I realized that most of the articles are focused on the ML part of the work. And there is not enough material on data engineering and pipelines. In this series of…
Production LLM: Agent with tools
In this article, we continue looking into the implementation of LLM-based agents. Please see Production LLM: how to harness the power of LLM in real-life business cases. to get more background…
Production LLM: SQL Agent
This article continues a series of articles where we talk about the production applications of LLMs. Please make sure to read the summary article explaining the context and structure of the…
Production LLM: Vector Retriever
This article is part of a series in which we create information retrieval chatbots and investigate possible technical solutions and their use cases. You can read more about the series here…
Production LLM: how to harness the power of LLM in real-life business cases.
It’s been a while since I wrote the last article. I am glad I can resume and continue describing Production Machine Learning implementations. I really hope it helps you, reader, solve…