In this article, you will learn how to get reliable structured outputs from large language models by validating them against a contract and the source text, retrying only when it helps, and constraining generation.
Archive | Language Models
What Are Diffusion Language Models, and Why Are They Different From GPT?
In this article, you will learn how diffusion language models generate text by iteratively refining a masked sequence, and how this differs from the token-by-token generation of GPT-style models.
Benchmarking Scikit-Ollama vs. Groq-Backed Scikit-LLM: Speed, Cost, and Accuracy
In this article, you will learn how to build a side-by-side benchmark of local and cloud LLM classifiers using Scikit-Ollama and a Groq-backed Scikit-LLM pipeline.
DiffusionGemma: Hands-On with Google’s Text Model That Writes All at Once
In this article, you will learn what DiffusionGemma is, how its diffusion-based text generation differs from standard autoregressive language models, and how to run it with Hugging Face Transformers and vLLM.
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 six-question decision tree.
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 training a scikit-learn classifier on frozen embeddings.
Speculative Decoding Explained: How to Get 3× Faster Inference from Any LLM
In this article, you will learn how speculative decoding works, why it produces real speed gains at the hardware level, and what modern implementations look like in practice.
pgvector vs. Dedicated Vector Databases: A Production Benchmark
In this article, you will learn how pgvector compares to dedicated vector databases in terms of real, measured performance, and how to decide which approach suits your production workload.
Choosing a Chunking Strategy for Your LLM Apps: A Decision-Tree Approach
In this article, you will learn how to choose the right chunking strategy for a retrieval-augmented generation system by working through a structured decision tree tied to the properties of your documents and retrieval task.
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 you full control over how your models are tested and scored.