From RAG to Agents: Prerequisites and Hard Limits of Agentic RAG
Agentic RAG is a stack with new failure modes, not an upgrade. Learn the prerequisites and the four physics that limit multi-step retrieval pipelines.
RAG Pipeline Design
Architecture patterns and retrieval strategies for building retrieval-augmented generation systems that ground LLM responses in external knowledge.
This theme is curated by our AI council — see how it works.
What topics does this domain cover?
6 topicsEach topic below is a key concept in this domain. Pick any for the full picture: foundations, implementation, what's changing, and risks to consider.
Agentic RAG →
Agentic RAG is a retrieval-augmented generation pattern where an LLM agent decides what to retrieve, when to retrieve …
5 articles
Contextual Retrieval →
Contextual retrieval is a set of techniques that enrich document chunks with surrounding context before indexing them …
5 articles
Hybrid Search →
Hybrid search combines two ways of finding documents: dense vector search, which matches by meaning, and sparse keyword …
7 articles
Query Transformation →
Query transformation is the set of techniques that rewrite, expand, or decompose a user's question before it reaches the …
8 articles
Reranking →
Reranking is a second-stage step in retrieval systems where a more accurate model rescores the top candidates returned …
6 articles
Retrieval-Augmented Generation →
Retrieval-Augmented Generation (RAG) is an architecture pattern that connects a large language model to an external …
7 articles
Four perspectives on this domain
MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.
Updated May 3, 2026
Concepts covered
Contextual Retrieval: How Prepended Context Reduces RAG Failures
Contextual retrieval prepends 50-100 tokens of LLM-generated context to each chunk before indexing. Anthropic reports a 67% drop in retrieval failures.
Contextual Retrieval: Prerequisites and Hard Limits at Scale
Contextual Retrieval cuts RAG failure rates, but at a cost. Learn the prerequisites — chunking, hybrid search, reranking — and where it breaks at scale.
What Is Agentic RAG and How LLM Agents Decide What to Retrieve
Agentic RAG turns retrieval into a decision: an LLM agent chooses whether to retrieve, which source to query, and whether the answer is good enough.
How HyDE, Multi-Query, and Step-Back Improve RAG Retrieval Recall
Query transformation rewrites user prompts before retrieval. Learn how HyDE, Multi-Query, and Step-Back Prompting close the question-answer geometry gap.
Cross-Encoder Reranker Limits: Latency Walls and Domain Drift
Cross-encoder rerankers hit two architectural walls: latency scales linearly with candidates and quadratically with tokens, plus MS-MARCO domain drift.
Cross-Encoders, Bi-Encoders, and Listwise Scoring in Reranking
A reranker reorders the top candidates from vector search using a heavier model. Cross-encoders, bi-encoders, and listwise scoring explained.
From Recall Failures to RAG-Fusion: Prerequisites and Inner Workings of Query Decomposition and Routing
Vector retrievers lose compound questions to a single point. Query decomposition, routing, and RAG-Fusion fix it by reshaping retrieval geometry.
Query Transformation Limits: Latency Tax, Drift, Hallucinated Documents
Query transformation in RAG hits three hard limits: latency tax from extra LLM calls, query drift on simple inputs, and hallucinated documents from HyDE.
What Is Reranking and Why Cross-Encoders Rescore RAG Retrieval
Reranking splits recall and precision into two stages. See how cross-encoders rescore retrieved documents and why a bi-encoder alone cannot match them.
BM25, SPLADE, and Reciprocal Rank Fusion: The Building Blocks of Production Hybrid Search
BM25, SPLADE, and reciprocal rank fusion each solve a different retrieval problem. Here's how the three combine into a production hybrid search system.
What Is Hybrid Search and How BM25 Plus Dense Vectors Beat Either Alone in RAG
Hybrid search fuses BM25 keyword retrieval with dense vector search using reciprocal rank fusion. Why two ranked lists beat either alone in RAG pipelines.
From Chunking to Reranking: RAG Pipeline Components and Prerequisites
Every RAG pipeline runs five components — chunker, embedder, vector store, retriever, reranker. Here is what each one does and where each one breaks.
Score Mismatch, Tuning Hell: The Hard Limits of Hybrid Search Fusion
Hybrid search merges BM25 and vector results, but the fusion step has hard limits. Score mismatch, RRF blindness, and tuning hell — explained.
What Is RAG and How LLMs Use Vector Search to Ground Their Answers
Retrieval-augmented generation pairs an LLM with a vector index so answers are grounded in real documents — not just training data. The mechanism, explained.
Why RAG Still Fails in Production: Retrieval, Chunking, Grounding
RAG fails in production because retrieval, chunking, and grounding hit structural limits — not because of bugs. Why correct retrieval still hallucinates.
