Reasoning & Memory
How models think, remember, and retrieve information. Reasoning tokens, RAG pipelines, context engineering, and the memory architectures that make agents useful.
Guides and explainers
Detailed guides and practical technical analysis.
Latest analysis
Recent research, benchmark reviews and technical updates.
Practical tools
Templates for budgets and project planning
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When to Use RAG vs Fine-Tuning in 2026: A Practitioner's Decision Guide
Most teams get this decision backwards. They pick RAG because it's the default, or fine-tuning because it sounds more sophisticated, then spend three months retrofitting the wrong architecture.
RAG vs Long Context vs Fine-Tuning: What Actually Works in Production
RAG vs long context vs fine-tuning: real production data on cost, latency, and accuracy. A practitioner's decision guide for 2026.
Pinecone vs Weaviate vs Qdrant vs Chroma: Vector Database Comparison 2026
A data-driven comparison of Pinecone, Weaviate, Qdrant, and Chroma covering benchmarks, pricing, and production trade-offs. Updated for 2026.
Your Agent's Memory Problem Isn't Where You Think
A diagnostic framework crossing three write strategies with three retrieval methods reveals that retrieval quality dominates agent memory performance.
Your Model Already Knows the Answer
Attention probes on DeepSeek-R1 and GPT-OSS show models reach their final answer far earlier than their chain-of-thought suggests. On easy questions, roughly 40% of reasoning tokens are pure performance.
Agentic RAG: How AI Agents Are Rewriting Retrieval
The old retrieve-once-generate-once pipeline is dead, and agents killed it. Four architectural patterns are reshaping how production systems handle knowledge retrieval.
Building RAG Systems That Actually Work
73% of enterprise RAG deployments fail, with 80% of failures traced to chunking decisions. This guide covers the implementation decisions that separate working RAG from abandoned prototypes.
Fine-Tuning vs RAG vs Prompt Engineering: A Decision Framework
Every AI builder hits the crossroads: better prompts, retrieval, or fine-tuning? This guide provides a concrete decision tree based on data freshness, accuracy needs, cost, and latency.
Chain-of-Thought Prompting: When It Works, When It Fails, and Why
Chain-of-thought is the most studied prompting technique in AI, and the most misapplied. A decision framework for when it helps, when it hurts, and what it costs.
LLMs Can't Find What's Already In Their Heads
Knowledge graphs have a well-documented lookup problem. When you ask an LLM to traverse a KG and reason over multi-hop paths, it doesn't search the graph...