Reasoning & Memory
Key Guides
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.
AI Evaluation Frameworks 2026: Why Benchmarks Keep Lying
GPT-5.3 Codex scores 99% on GSM8K. Frontier models cluster above 90% on MMLU. OpenAI retired SWE-bench Verified in February 2026 after auditing 27.6% of the dataset and finding that at least 59.4% of the audited problems had flawed test cases that rejected correct submissions. The benchmarks that
Best RAG Frameworks and Tools 2026: From Prototype to Production
Framework choice determines whether your RAG system actually works. The gap between a demo and a production system that handles messy documents at scale is enormous. Eight frameworks that matter in 2026.
RAG for Legal: Building Document Retrieval That Survives Court
More than 300 documented instances of AI-generated fake citations have appeared in court filings since mid-2023. The question isn't whether to use AI for legal research — it's how to build retrieval systems that hold up under adversarial scrutiny.
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.
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...