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In this article, you will learn how inference caching works in large language models and how to use it to reduce cost and latency in production systems. Topics we will cover include: The fundamentals of inference caching and why it matters The three main caching types: KV caching, prefix caching, and semantic caching How to…
In this article, you will learn how to use Python decorators to improve the reliability, observability, and efficiency of machine learning systems in production. Topics we will cover include: Implementing retry logic with exponential backoff for unstable external dependencies. Validating inputs and enforcing schemas before model inference. Optimizing performance with caching, memory guards, and monitoring…
In this article, you will learn how to build efficient long-context retrieval-augmented generation (RAG) systems using modern techniques that address attention limitations and cost challenges. Topics we will cover include: How reranking mitigates the “Lost in the Middle” problem. How context caching reduces latency and computational cost. How hybrid retrieval, metadata filtering, and query expansion…
In this article, you will learn the architectural differences between structured outputs and function calling in modern language model systems. Topics we will cover include: How structured outputs and function calling work under the hood. When to use each approach in real-world machine learning systems. The performance, cost, and reliability trade-offs between the two. Structured…
In this article, you will learn how to build a local, privacy-first tool-calling agent using the Gemma 4 model family and Ollama. Topics we will cover include: An overview of the Gemma 4 model family and its capabilities. How tool calling enables language models to interact with external functions. How to implement a local tool…
In this article, you will learn how to build a deterministic, multi-tier retrieval-augmented generation system using knowledge graphs and vector databases. Topics we will cover include: Designing a three-tier retrieval hierarchy for factual accuracy. Implementing a lightweight knowledge graph. Using prompt-enforced rules to resolve retrieval conflicts deterministically. Beyond Vector Search: Building a Deterministic 3-Tiered Graph-RAG…
In this article, you will learn how to evaluate large language model applications using RAGAs and G-Eval-based frameworks in a practical, hands-on workflow. Topics we will cover include: How to use RAGAs to measure faithfulness and answer relevancy in retrieval-augmented systems. How to structure evaluation datasets and integrate them into a testing pipeline. How to…
In this article, you will learn how to systematically select and apply agentic AI design patterns to build reliable, scalable agent systems. Topics we will cover include: Why design patterns are essential for predictable agent behavior Core agentic patterns such as ReAct, Reflection, Planning, and Tool Use How to evaluate, scale, and safely deploy agentic…
In this article, you will learn how to use Python’s itertools module to simplify common feature engineering tasks with clean, efficient patterns. Topics we will cover include: Generating interaction, polynomial, and cumulative features with itertools. Building lookup grids, lag windows, and grouped aggregates for structured data workflows. Using iterator-based tools to write cleaner, more composable…
In this article, you will learn how reranking improves the relevance of results in retrieval-augmented generation (RAG) systems by going beyond what retrievers alone can achieve. Topics we will cover include: How rerankers refine retriever outputs to deliver better answers Five top reranker models to test in 2026 Final thoughts on choosing the right reranker…
In this article, you will learn how to identify, understand, and mitigate race conditions in multi-agent orchestration systems. Topics we will cover include: What race conditions look like in multi-agent environments Architectural patterns for preventing shared-state conflicts Practical strategies like idempotency, locking, and concurrency testing Let’s get straight to it. Handling Race Conditions in Multi-Agent…
In the previous article, we saw how a language model converts logits into probabilities and samples the next token. But where do these logits come from? In this tutorial, we take a hands-on approach to understand the generation pipeline: How the prefill phase processes your entire prompt in a single parallel pass How the decode…