Reasoning & Memory
How models think, remember, and retrieve information. Reasoning tokens, RAG pipelines, context engineering, and the memory architectures that make agents useful.
Key Guides
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.
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...
Small Models Just Got Smarter About When to Think
Reasoning tokens aren't free. Every chain-of-thought step an LLM generates costs inference budget, and most of the time that thinking is wasted on tasks...
Inference-Time Scaling: Why AI Models Now Think for Minutes Before Answering
OpenAI's o1 model spends 60 seconds reasoning through complex problems before generating a response. GPT-4 responds in roughly 2 seconds. This isn't a...
Vector Databases Are Agent Memory. Treat Them Like It
Most teams treat vector databases as fancy search indexes. The teams building agents that actually remember treat them as memory systems: with tiered architecture, decay policies, and retrieval strategies that mirror how memory actually works.
RAG Architecture Patterns: From Naive Pipelines to Agentic Loops
The naive RAG pipeline fails silently on every query that requires reasoning. From iterative retrieval to agentic loops, here are the architecture patterns that separate demos from production systems.
Context Is The New Prompt
Prompt engineering hit its ceiling. The teams pulling ahead now are engineering context: retrieval, memory, tool access, not tweaking instructions. Context is the new prompt.
The RAG Reliability Gap: Why Retrieval Doesn't Guarantee Truth
RAG is the industry's default answer to hallucination. The research says it's not enough.