AI Implementation Blog

Why Naive Chunking Breaks RAG: AST vs CST for Real Retrieval

A practical breakdown of AST vs CST chunking, why naive token splits fail for structured artifacts, and when hybrid indexing wins.

Why Off-the-Shelf Chunkers Silently Break Internal RAG

Generic text splitters optimize for token budget—not organizational knowledge. When internal docs prescribe how things should be done, default chunkers destroy policy and intent.

Chunking Is Knowledge Modeling, Not Preprocessing

Treating chunking as a "split text" step guarantees retrieval failures. Internal RAG requires extracting artifacts as first-class knowledge units with explicit metadata.

Your RAG System Won't Know What Your Org Cares About (Unless You Tell It)

Embeddings can't infer organizational context. If your chunking logic doesn't encode your org's opinions, your retrieval system won't either—and no prompt tuning fixes that.

From Tokens to Truth: Designing Production-Grade Hybrid RAG Systems

Why tokenization matters, why you can't mix tokenizers inside a single model, and how to build dense+sparse retrieval with fusion, reranking, and guardrails.

How RAG Works — and When It Fails

A practical retrieval-success playbook: why retrieval breaks in production and concrete patterns (hybrid search, metadata filtering, reranking, multi-hop) that make RAG reliable.

How to Build a Multi-Model Vision Pipeline

A practical guide to building a production-grade vision system using a router model, specialized experts, and a generalist model.

Why We Still Need Traditional Classifiers

A technical deep dive into why LLMs need specialized traditional models for vision pipelines: YOLO, classifiers, and embeddings.

Batching vs Incremental Runs

A conversational guide to batching vs incremental runs, idempotency, and schema/versioning for reliable data and agentic pipelines.

LangGraph vs LangChain

State graphs, nodes, edges, checkpoints, persistence, and threading—how they fit together and when to use them.

How LLMs “Remember”

Context windows, reflection loops, and vector memory (Chroma, Milvus, BilberryDB) for practical agents.

Social Reflexion

A consent-first layer that routes expertise across teams using reflection data, relevance matching, and notifications—with opt-in privacy and sensitivity filters.