The journal

How the context layer is built.

Benchmarks, retrieval architecture and the engineering behind indexing a codebase once and serving it to every tool that asks.

All 86 Customers 20 Engineering 66

41–50 of 66 · page 5 of 7

Mar 31, 2026 softmax attention dilution context rot

Softmax Attention and the Dilution Problem: The Math Behind Context Rot

As context grows, softmax normalizes attention weights so each relevant token gets less attention. This mathematical property is why AI accuracy drops with length.

Mar 31, 2026 sparse attention Longformer Linformer

Sparse Attention Architectures: Breaking O(n²) with O(n√n), O(n·log n), and O(n)

Multiple research approaches attack the quadratic bottleneck: Longformer, Reformer, Linformer, and linear attention. Here's the math behind each one.

Mar 31, 2026 transformer attention mechanism query key value

How the Transformer Attention Mechanism Actually Works (No Math Required)

The attention mechanism is the beating heart of every LLM. Here's how it decides which parts of your conversation matter most — explained with analogies before equations.

Mar 31, 2026 tokens tokenization BPE

Tokens Explained: Why Your AI Counts Words Differently Than You Do

AI doesn't read words — it reads tokens. Learn how tokenization works, why code costs more than prose, and how to calculate your token usage.

Mar 31, 2026 file upload document tokenization Claude

What Happens Inside the Context Window When You Upload a File to Claude

When you upload a PDF or code file, it gets converted to tokens and placed directly into the context window — consuming the same limited space as your messages.

Mar 31, 2026 AI memory stateless systems RAG

Why Does My AI Forget Everything Between Sessions?

AI models are stateless. Each conversation is a fresh start. There's no database storing what you said yesterday. Here's why — and what it means for you.

Mar 31, 2026 RAG retrieval augmented generation long context

Why RAG Exists: Context Windows Are Too Small for Your Data

If context windows were infinite, free, and didn't degrade, we wouldn't need RAG. Here's why retrieval-augmented generation exists and when to use it.

Jan 15, 2026 context graph context management cross-repository AI

Why "Context Graph" Has Become the Most Misunderstood Term in AI Engineering

A technical deep-dive into LLM context management, the computational limits of context windows, and why every AI tool's 'context graph' solution might just be clever marketing around semantic search and RAG.

Jan 5, 2026 end-to-end testing system-level evaluation error compounding

End-to-End System Evaluation: The Stress Test of GraphRAG

Individual layers may pass, but systems often fail at the seams. This blog details how to conduct holistic 'System-in-the-Loop' tests, measuring how retrieval noise compounds into generation errors across 25+ repositories. We provide a blueprint for evaluating the full journey from a vague natural language query to a multi-repo pull request.

Jan 2, 2026 generation faithfulness code grounding LLM-as-a-Judge

Evaluating Generation and Grounding in Multi-Repo Systems

Retrieving nodes is only half the battle; the LLM must synthesize code that adheres to cross-repo constraints. This post explores measuring faithfulness, checking execution-level correctness against internal SDKs, and using LLM-as-a-Judge to verify that generated code respects the security and type contracts of separate repositories.