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
BoredTools offers practical spreadsheets for budgets, freelance work and small projects.
Agent Memory Architecture: Long-Term, Episodic, and Semantic Memory for AI Agents
After a year of ad-hoc RAG solutions, agent memory is becoming a proper engineering discipline. Four independent research efforts outline budget tiers, shared memory banks, empirical grounding, and temporal awareness: the building blocks of a real memory architecture.
RAG Pipelines Are Silently Dropping Context
Your RAG pipeline retrieves the right documents. The LLM ignores half of them. The RAG-E framework found generators skip the top-ranked passage in 47-67% of cases. The retrieval-utilization gap is the real bottleneck.
Choosing Between RAG, Long Context, and Fine-Tuning
Compare RAG, long-context windows, and fine-tuning on accuracy, cost, latency, and production readiness.
AI Evaluation Frameworks 2026: Why Benchmarks Keep Lying
AI benchmarks are broken. Contaminated datasets, narrow metrics, and Goodhart's law mean top scores rarely predict real-world performance. Here is what evaluation frameworks actually need to measure in 2026.
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.
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 Fine-Tuning vs Long Context: A Practical Guide
Choose retrieval, long context or fine-tuning with a practical test matrix for evidence, freshness and cost.
Pinecone vs Weaviate vs Qdrant vs Chroma (2026)
Compare vector database deployment and retrieval options, including pgvector, Azure AI Search and Pinecone filtering. A practical evaluation guide.
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.