LLM Architecture5 articles

LLM Architecture

Articles

GraphRAG Frameworks in Production: Comparing Microsoft GraphRAG, LightRAG, Fast-GraphRAG, and Neo4j Hybrid Architectures
Standard dense vector retrieval fails on two specific query topologies: global corpus sense-making and multi-hop associative entity traversal. Standard vector search relies on flat chunk embeddings (cosine similarity over top-k chunks), which isolates information into disconnected fragments. If a query requires connecting entity A to entity C through intermediate entity B across documents, or synthesizing thematic patterns across an entire million-token repository, vector databases return disjoi
August 27, 20261 min
Hierarchical Tree-Organized Retrieval (RAPTOR) in Production RAG: Recursive Summarization, Gaussian Mixture Clustering, and Cross-Scale Querying
Standard retrieval-augmented generation (RAG) architectures operate on flat document chunks. Corpora are split into fixed token windows (typically 256 to 1024 tokens), mapped into vector space via dense embedding models, and queried through approximate nearest neighbor (ANN) search. While this setup efficiently resolves localized factual lookups ("What is the termination clause in contract X?"), it systematically fails on thematic synthesis, cross-document comparison, and high-level aggregation
August 24, 20261 min
Listwise Reranking in Production: Comparing Pointwise, Pairwise, and Listwise LLM Architectures, Sliding-Window Permutations, and Serving Economics
Information retrieval systems in production Retrieval-Augmented Generation (RAG) and enterprise search have transitioned through multiple reranking paradigms. While first-stage retrieval (dense vector embeddings and sparse lexical BM25/SPLADE) retrieves candidate sets of 50 to 200 documents in under 20 milliseconds, the precision of downstream generation depends heavily on the reranking stage. Traditional neural rerankers evaluate candidates through pointwise scoring or pairwise classification.
August 22, 20261 min
Query Transformation in Production RAG: Architecture, Latency Economics, and Retrieval Trade-Offs for HyDE, Multi-Query Expansion, and Step-Back Prompting
Standard retrieval-augmented generation (RAG) architectures operate on a naive assumption: that the raw user query is suitable for direct retrieval against a vector database or lexical search index. In production, this assumption fails across significant query distributions. Raw user queries are frequently short (averaging 4 to 8 words), structurally underspecified, conversational, or laden with unresolved pronoun bindings. Conversely, indexed document chunks typically contain 256 to 1024 tokens
August 20, 20261 min
Context Compaction and Session Pruning in Production AI Agents: Architecture, Hierarchical Summarization, and Constraint Preservation
Context Compaction and Session Pruning in Production AI Agents: Architecture, Hierarchical Summarization, and Constraint Preservation Long-running autonomous agents executing multi-step workflows (codebase refactoring, recursive research, multi-turn debugging, and system operations) face an inevitable physical ceiling: working context exhaustion. As an agent executes shell commands, inspects repository trees, parses LSP diagnostics, and consumes tool outputs, the active context window fills rap
August 20, 20261 min

LLM Architecture

GraphRAG Frameworks in Production: Comparing Microsoft GraphRAG, LightRAG, Fast-GraphRAG, and Neo4j Hybrid Architectures

Hierarchical Tree-Organized Retrieval (RAPTOR) in Production RAG: Recursive Summarization, Gaussian Mixture Clustering, and Cross-Scale Querying

Listwise Reranking in Production: Comparing Pointwise, Pairwise, and Listwise LLM Architectures, Sliding-Window Permutations, and Serving Economics

Query Transformation in Production RAG: Architecture, Latency Economics, and Retrieval Trade-Offs for HyDE, Multi-Query Expansion, and Step-Back Prompting

Context Compaction and Session Pruning in Production AI Agents: Architecture, Hierarchical Summarization, and Constraint Preservation