In this article, you will learn how to design AI agents that can reliably self-correct by grounding their feedback loops in external verification rather than the model’s own judgment.
7 Chunking Strategies That Decide Whether Your RAG Works
Discover 7 chunking strategies for RAG pipelines and learn which approach fits your document type and query needs.
Measuring Performance of Transformer Inference
When you optimize the inference performance of an LLM, you need to know how to measure it. Without measurement, it is easy to make a model more complicated without making it faster, or to improve throughput while making user-visible latency worse. An LLM service has several kinds of performance. A user cares about how long […]
Static vs. Dynamic vs. Continuous Batching in LLM Inference
In this article, you will learn how static, dynamic, and continuous batching work in LLM inference, and why the differences between them matter at production scale.
Decoding Strategies and Output Control
A language model does not write text directly. Instead, it returns logits for the next token. The decoding algorithm decides how to turn those logits into a token, and repeating this decision produces the output text. The decoding algorithm affects the behavior of the model. Greedy decoding is deterministic and stable, but it can be […]
Using a Transformer Model: From Training to Inference
If you have implemented a transformer model in PyTorch, you can use the same code for both training and inference, but in very different ways. During training, you usually process a batch of fixed-length token sequences and update the model weights. During inference, the weights are fixed and the model generates new tokens one at […]
The End-to-End Agentic AI Pipeline
In this article, you will learn the seven architectural components that separate a production-grade agentic AI system from a demo script, and how each one fits into the agent’s core feedback loop.
Ollama vs. LM Studio vs. llama.cpp: Which Local AI Runtime Should You Use in 2026?
Compare Ollama, LM Studio, and llama.cpp across five key dimensions to find the right local AI runtime for your workflow.
5 Architectural Patterns for Persistent Memory and State in AI Agents
In this article, you will learn the five core architectural patterns for managing persistent memory and state in AI agents, and why treating them as deliberate design decisions is essential for production-grade systems.
Stateful vs. Stateless Agent Design: Tradeoffs for Scalable Agentic Systems
In this article, you will learn how an agent’s approach to managing state — stateless or stateful — shapes both its implementation and the deployment architecture built around it.