· 4 min read
RAG vs Long Context: When to Retrieve, When to Stuff
Skip the 'long context killed RAG' debate. Here's a practical decision framework based on cost, latency, recall, and freshness.
All#ai#ai-agents#ai-productivity#applied-ai#automation#calendar#context-switching#deep-work#documents#email#engineering#engineering-leadership#invisible-ai#knowledge-work#llm-evaluation#product-updates#productivity#prompt-engineering#remote-work#search#security#spreadsheets#startups#storage#tamaton#unified-workspace#workflow#workflows
· 4 min read
Beyond Chatbots: Pragmatic AI Workflows for Document Management
Embedded AI that categorizes files and drafts replies from your own data beats generic prompts. Here's how to build document workflows that actually save time.
· 4 min read
Why Your RAG Pipeline Retrieves the Wrong Chunks
Most RAG failures aren't the LLM's fault. Here's a diagnostic framework that traces bad answers back to chunking, embeddings, and query rewriting.
· 4 min read
Memory Systems for Long-Running AI Productivity Agents
Architectural patterns that give agents persistent memory across emails, documents, and projects — without exponential token costs.
· 4 min read
The Latency Tax: Why Unified Data Layers Beat App-Switching
Context switching isn't just a human problem. For AI agents, it's a measurable performance tax that only a unified data layer can eliminate.
· 3 min read
Unified Search Architecture: How Tamaton Queries Across Silos
A technical deep-dive into Tamaton's vector search implementation that seamlessly queries across email, documents, and files using embedded representations.
· 3 min read
Why RAG Pipelines Fail: 7 Architecture Patterns That Work
RAG systems break in predictable ways. Here's how production teams architect around common failure modes with proven patterns.
· 3 min read
Intent-Based Search: Vector Embeddings Replace Keywords
Traditional keyword search fails when you can't remember exact terms. Semantic search understands what you meant to find.
· 3 min read
Hybrid Search: Why Vector Embeddings Alone Fail Professional Users
Professional productivity demands more than semantic similarity. Here's why combining LLM-extracted metadata with keyword search delivers the precision knowledge workers need.
· 3 min read
From Folders to Context: Why RAG Redefines File Discovery
Retrieval-Augmented Generation transforms how AI agents find information by understanding meaning over memorizing paths.
· 4 min read
How AI Agents Use Tamaton's Unified Search to Query 10K+ Documents
A practical guide for AI agents leveraging Tamaton's cross-platform search API to instantly access emails, docs, and files without context switching.
· 3 min read
RAG vs Fine-tuning: When Each Approach Wins for Productivity Tools
A practical framework comparing retrieval-augmented generation and fine-tuning for document search, email classification, and knowledge base queries in productivity tools.
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