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How to Build a Privacy-Preserving Federated Pipeline to Fine-Tune Large Language Models with LoRA Using Flower and PEFT

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Federated Learning, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we demonstrate how to federate fine-tuning of a large language model using LoRA without ever centralizing private text data. We simulate multiple organizations as virtual clients and show how each client adapts a shared base model locally while exchanging only lightweight LoRA adapter parameters. By combining Flower’s federated learning simulation engine with […]

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A Coding Implementation to Establish Rigorous Prompt Versioning and Regression Testing Workflows for Large Language Models using MLflow

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Large Language Model, Staff, Technology, Tutorials

In this tutorial, we show how we treat prompts as first-class, versioned artifacts and apply rigorous regression testing to large language model behavior using MLflow. We design an evaluation pipeline that logs prompt versions, prompt diffs, model outputs, and multiple quality metrics in a fully reproducible manner. By combining classical text metrics with semantic similarity

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How to Design Production-Grade Mock Data Pipelines Using Polyfactory with Dataclasses, Pydantic, Attrs, and Nested Models

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we walk through an advanced, end-to-end exploration of Polyfactory, focusing on how we can generate rich, realistic mock data directly from Python type hints. We start by setting up the environment and progressively build factories for data classes, Pydantic models, and attrs-based classes, while demonstrating customization, overrides, calculated fields, and the generation

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How to Build a Production-Grade Agentic AI System with Hybrid Retrieval, Provenance-First Citations, Repair Loops, and Episodic Memory

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Staff, Tutorials

In this tutorial, we build an ultra-advanced agentic AI workflow that behaves like a production-grade research and reasoning system rather than a single prompt call. We ingest real web sources asynchronously, split them into provenance-tracked chunks, and run hybrid retrieval using both TF-IDF (sparse) and OpenAI embeddings (dense), then fuse results for higher recall and

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A Coding, Data-Driven Guide to Measuring, Visualizing, and Enforcing Cognitive Complexity in Python Projects Using complexipy

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Visualization, Databases, Editors Pick, Staff, Tutorials

In this tutorial, we build an end-to-end cognitive complexity analysis workflow using complexipy. We start by measuring complexity directly from raw code strings, then scale the same analysis to individual files and an entire project directory. Along the way, we generate machine-readable reports, normalize them into structured DataFrames, and visualize complexity distributions to understand how

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How to Build Production-Grade Data Validation Pipelines Using Pandera, Typed Schemas, and Composable DataFrame Contracts

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Visualization, Databases, Editors Pick, Staff, Tutorials

Schemas, and Composable DataFrame ContractsIn this tutorial, we demonstrate how to build robust, production-grade data validation pipelines using Pandera with typed DataFrame models. We start by simulating realistic, imperfect transactional data and progressively enforce strict schema constraints, column-level rules, and cross-column business logic using declarative checks. We show how lazy validation helps us surface multiple

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How to Build Efficient Agentic Reasoning Systems by Dynamically Pruning Multiple Chain-of-Thought Paths Without Losing Accuracy

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Staff, Tutorials, Uncategorized

In this tutorial, we implement an agentic chain-of-thought pruning framework that generates multiple reasoning paths in parallel and dynamically reduces them using consensus signals and early stopping. We focus on improving reasoning efficiency by reducing unnecessary token usage while preserving answer correctness, demonstrating that self-consistency and lightweight graph-based agreement can serve as effective proxies for

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A Coding Implementation to Train Safety-Critical Reinforcement Learning Agents Offline Using Conservative Q-Learning with d3rlpy and Fixed Historical Data

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Editors Pick, Machine Learning, Technology, Tutorials

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a behavior dataset from a constrained policy, and then train both a Behavior Cloning baseline and a Conservative Q-Learning agent using d3rlpy. By structuring the workflow around offline

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How to Build Advanced Quantum Algorithms Using Qrisp with Grover Search, Quantum Phase Estimation, and QAOA

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we present an advanced, hands-on tutorial that demonstrates how we use Qrisp to build and execute non-trivial quantum algorithms. We walk through core Qrisp abstractions for quantum data, construct entangled states, and then progressively implement Grover’s search with automatic uncomputation, Quantum Phase Estimation, and a full QAOA workflow for the MaxCut problem.

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How to Build Multi-Layered LLM Safety Filters to Defend Against Adaptive, Paraphrased, and Adversarial Prompt Attacks

ai, AI (Artificial Intelligence), Artificial Intelligence, Context Engineering, Editors Pick, prompt engineering, Security, Staff, Technology, Tutorials, Uncategorized

In this tutorial, we build a robust, multi-layered safety filter designed to defend large language models against adaptive and paraphrased attacks. We combine semantic similarity analysis, rule-based pattern detection, LLM-driven intent classification, and anomaly detection to create a defense system that relies on no single point of failure. Also, we demonstrate how practical, production-style safety

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