Retrieval & RAG

Advanced RAG Architectures

The naive→modular→agentic RAG spectrum and the levers that matter — CRAG, Self-RAG, query transformation, fusion, reranking — all attacking the same garbage-in/confident-wrong-out failure.

GraphRAG & Multi-Hop Retrieval

Why flat top-k RAG cannot answer thematic or relational multi-hop queries, how Microsoft GraphRAG and iterative retrieve-reason loops solve it, and the cost/staleness heuristic for when not to.

Hybrid Search & Reranking

Why one retriever is structurally not enough, reciprocal rank fusion across BM25 + dense, the two-stage retrieve-then-cross-encoder pattern, and ColBERT-style late interaction when cross-encoders are too slow.

Document Parsing & Ingestion Quality

Ingestion is the half of RAG that doesn't get dashboards and decides whether the answer was ever indexable — layout-aware parsing, tables, OCR, structural chunking, and vision-RAG as a parsing escape hatch.

Query Understanding & Transformation

Fixing the question before you search — rewriting, decomposition, multi-query, HyDE caveats, step-back prompting, and routing — the cheapest place to add intelligence to a RAG pipeline.

Agentic Retrieval: Search as a Tool

Retrieval as a tool the model calls iteratively in a ReAct-style loop, with budget, stopping criteria, and the new failure modes (looping, drift, premature stop) that come with handing the model the steering wheel.

Evaluating RAG

Score retrieval, grounding, and answer quality as three separate things — recall@k, faithfulness, answer relevance — plus how to build a small living eval set and use LLM judges without lying to yourself.

Choosing a Vector Database

A constraint-first selection guide — what a vector DB actually is, the axes that genuinely differ between products (ANN algorithm, filter quality, freshness, hybrid, multi-tenancy, ops, cost), just enough HNSW/IVF/DiskANN internals to read a vendor pitch, and why most teams end at pgvector.

Local-First Retrieval

Building a knowledge base on hardware you own — starting with whether you need an index at all, since the leading coding agents removed theirs in favour of grep. Ingest quality, embedding-model sizing, the four store architectures, hybrid retrieval, serving it to an agent over MCP, and when to run a finished platform instead.

Index Freshness & Invalidation

A stale index returns a confident answer with a citation, and the three ways a corpus goes stale have different blast radii — an out-of-date paragraph is embarrassing, a deleted document that still answers is an incident. Why the nightly re-crawl is a bill rather than a guarantee, how to drive invalidation from a change stream that is reliably bad at deletions, tombstones before compaction, the derived artefacts that inherit no deletion at all, and change-to-queryable as a per-source p99 with a name on it.

Contextual Retrieval

The largest source of recall failure in a competent RAG stack is the moment you cut the document up: the chunk that says revenue grew 3% names no company, no quarter and no year. Prepending a generated preamble before embedding cuts top-20 retrieval failure by 35%, 49% when the same preamble feeds BM25, and 67% with reranking on top (5.7% to 1.9%), at roughly $1.02 per million document tokens under prompt caching — but your index is now a function of a second model and a prompt you will want to change, so budget three rebuilds and try heading-path prefixes, a reranker and hybrid search first.

Late-Interaction Retrieval

The standard objection — one vector per token is a hundred times the storage — has been obsolete for four years and still decides architectures: ColBERTv2's residual compression takes MS MARCO from 154 GiB to 25 GiB at two bits per dimension, the same order as a plain float32 single-vector index over the same 8.8 million passages, and token pooling removes half the vectors again at virtually no measured cost. So the real decision is diagnostic — a better first stage only fixes recall failures, and a 2026 separation result says the queries where single vectors structurally fail are the multi-constraint conjunctive ones an agent actually issues. For page images the comparison is not against your retriever at all; it is against the parsing pipeline ColPali-style retrieval deletes.