reasoning-memory
From the team behind Swarm Signal
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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.
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.
How to Evaluate AI Models Without Trusting Benchmarks
Benchmarks are contaminated, gamed, and misleading. Here's how to build evaluation systems that predict real-world model performance.
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.
More Context Doesn't Kill RAG. It Just Changes the Fight.
Long-context LLMs now hit a million tokens, but a persistent 10% accuracy gap and punishing costs keep RAG very much in the fight.