In this article, you will learn seven common token-waste patterns in production AI agent systems, and the architectural fixes that address each one.
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.
Context Managers Beyond Opening Files in Python
In this article, you will learn how Python context managers work beneath the surface, and how to apply them to real cleanup problems well beyond file handling.
Robust Serialization with JSON and Custom Types
In this article, you will learn how to build a robust JSON serialization pipeline in Python that handles custom data types, graceful error recovery, and schema migrations.
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.
How to Build a Multi-Modal RAG Pipeline for Images and Documents in Python
Build a multi-modal RAG pipeline in Python to retrieve both text and images from complex documents.
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.
Descriptors and Controlled Attributes in Python
In this article, you will learn what Python descriptors are and how to use them to build three types of controlled attributes: validated, computed, and lazily loaded.
Iterators and Generators for Memory-Efficient Processing
In this article, you will learn how Python’s iterators and generators can be used to build memory-efficient data processing pipelines that handle large files without loading them into memory all at once.
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.