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How to Train Your own Decision Model like Jev using Unsloth

How to Train Your own Decision Model like Jev using Unsloth

In this article, you will learn how to turn an open-weight language model into a decision model that classifies user intent, by fine-tuning Qwen3.5-4B on…

Choosing an LLM Inference Optimization Technique: A Decision-Tree Approach

Choosing an LLM Inference Optimization Technique: A Decision-Tree Approach

In this article, you will learn how to match an LLM inference optimization technique to the actual bottleneck in your workload by working through a…

The AI Agent Memory Stack: Building Blocks, Patterns, and Frameworks

The AI Agent Memory Stack: Building Blocks, Patterns, and Frameworks

In this article, you will learn how agent memory works, what types of memory exist, and how different frameworks approach storage, retrieval, and memory management…

7 Ways Your AI Agent Is Wasting Tokens (And What to Do Instead)

7 Ways Your AI Agent Is Wasting Tokens (And What to Do Instead)

In this article, you will learn seven common token-waste patterns in production AI agent systems, and the architectural fixes that address each one. Topics we…

Fine-Tuning vs. Frozen Embeddings: When to Adapt the LLM vs. the Sklearn Head

Fine-Tuning vs. Frozen Embeddings: When to Adapt the LLM vs. the Sklearn Head

In this article, you will learn how to compare two strategies for adapting a large language model to a specific domain: fine-tuning with LoRA versus…

Building a Custom LLM Evaluation Harness in Python from Scratch

Building a Custom LLM Evaluation Harness in Python from Scratch

In this article, you will learn how to build a custom LLM evaluation harness in Python from scratch, structured around four modular components that give…

Building a RAG Pipeline with LLM Embeddings and Scikit-learn

Building a RAG Pipeline with LLM Embeddings and Scikit-learn

In this article, you will learn how to build a lightweight, end-to-end RAG pipeline using scikit-learn for vector retrieval and open-source models for embeddings and…

AI Agent Benchmarks Explained: SWE-Bench, GAIA, WebArena, and More

AI Agent Benchmarks Explained: SWE-Bench, GAIA, WebArena, and More

In this article, you will learn what the major AI agent benchmarks measure, how they differ from traditional model benchmarks, and how to use them…

How to Fine-Tune Llama 3 for Custom Tool Calling with Unsloth in Python

How to Fine-Tune Llama 3 for Custom Tool Calling with Unsloth in Python

Llama 3 is a capable generalist, but that’s exactly the problem when you need an agent that reliably outputs structured JSON for a specific API.

Evaluating Graph-RAG vs. Standard RAG: A Hallucination Benchmark on Fact-Dense Queries

Evaluating Graph-RAG vs. Standard RAG: A Hallucination Benchmark on Fact-Dense Queries

In this article, you will learn how to benchmark a deterministic 3-Tiered Graph-RAG system against a standard vector RAG pipeline on fact-dense queries, and what…

Choosing the Right Agentic AI Framework for 2026: A Decision-Tree Approach

Choosing the Right Agentic AI Framework for 2026: A Decision-Tree Approach

In this article, you will learn how to choose the right agentic AI framework for your production system by working through a structured decision-tree based…

Synchronous vs. Asynchronous Agent Execution: Architecture Patterns for Production

Synchronous vs. Asynchronous Agent Execution: Architecture Patterns for Production

In this article, you will learn how synchronous and asynchronous execution patterns differ architecturally, and how to choose between them when deploying LLM-based agents to…

How (and Why) to Build an AI Agent from Scratch in Python

How (and Why) to Build an AI Agent from Scratch in Python

In this article, you will learn what an AI agent is and how to build one from scratch in plain Python using the Anthropic API,…

Adding Temporal Reasoning to Graph-RAG: Tracking Fact Freshness and Staleness

Adding Temporal Reasoning to Graph-RAG: Tracking Fact Freshness and Staleness

In this article, you will learn how to add a lightweight temporal reasoning layer to a Graph-RAG system so that it can distinguish fresh facts…

Local Agentic AI Workflows with Hermes + Ollama

Local Agentic AI Workflows with Hermes + Ollama

In this article, you will learn how to build a fully local, zero-cost agentic AI workflow using Hermes Agent and Ollama, so that your files,…

Tool Calling vs. Code Execution for AI Agents: Choosing the Right Action Primitive

Tool Calling vs. Code Execution for AI Agents: Choosing the Right Action Primitive

Theory is easier to trust once it’s running against a real API, so both examples in this article use the same tool — a get_weather function backed by

Agent or Workflow? A Practical Test for Knowing When You Actually Need an AI Agent

Agent or Workflow? A Practical Test for Knowing When You Actually Need an AI Agent

In this article, you will learn the key differences between AI workflows and agents, and how to decide which approach is right for your use…

RAG vs. Fine-Tuning for Domain Adaptation: When to Use Which

RAG vs. Fine-Tuning for Domain Adaptation: When to Use Which

In this article, you will learn the mechanical difference between retrieval-augmented generation and fine-tuning, when each technique is the right tool, and how to decide…

Monitoring Embedding Drift in Production Scikit-LLM Pipelines

Monitoring Embedding Drift in Production Scikit-LLM Pipelines

In this article, you will learn what embedding drift is, why it matters for production large language models, and how to implement two practical techniques…

Dataclasses for Structured Application Data

Dataclasses for Structured Application Data

Scalar defaults work the way you’d expect, and batch_size: int = 500 is all you need.