RAG
Retrieval-augmented generation - how agents access external knowledge to ground their responses in facts instead of hallucinations.
Deep Dives and Frameworks
Implementation playbooks, operator patterns, and durable analysis.
Signals, Maps, and Watch Lists
Production-oriented analysis, benchmarks, and market/system intelligence.
External tools
Execution tooling is separate
Swarm Signal keeps the analysis layer. Use BoredTools for reusable production templates and trackers.
RAG Cost Attacks Turn Retrieval Into a Budget Risk
A June 2026 paper on retrieval-augmented inference cost attacks reports a failure mode that many RAG teams are not testing: poisoned external documents...
RAG Maintenance After Deployment: The Failure Mode Nobody Budgets For
RAG maintenance after deployment is the hidden operating cost: stale indexes, drifting corpora, weak evals, and silent retrieval failure.
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.
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 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.
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.