The Dawn of HAG: Human Augmented Retrieval + RAG CAG and MAG

Augmented Generation in AI

Watching the unfolding of a new way of work is breathtaking. The agentic age seems to coin new terms and acronyms daily. In the development of augmented retrieval, we can now see the outline of what I call HAG: Human Augmented Generation. This is non other that the Navigator-Driver paradigm as explained by the thought provoking articles by Peter Heller, who has spent a lifetime working with computers.

At the beginning, middle and end of the retrieval story is HAG and this is closely integrated with the last pillar of Ash Tilawat’s four pillars of AI: RAG, Graph, Agents, and Evals. In my newest book, we can learn to capture human decision and judgement using a novel technique which I call AI Charting which I hope to build out over the coming months.

Let’s cover each retrieval method. All code for the article can be downloaded here:

HAG (Human Augmented Generation)

The human brain itself is a quantum computer. Any initial design that comes from this brain is a constant at the beginning, middle and end of each epoch. It is the essential constant in the ebb and flow of progress.**

RAG (Retrieval Augmented Generation)**

RAG processes described by DeepLearningAI

Use Retrieval‑Augmented Generation when you need models to answer queries using large, specific, or frequently changing external knowledge (e.g., proprietary docs, product catalogs, legal/medical texts, or recent news) while limiting hallucinations and prompt length. It’s ideal for grounding responses, supporting long‑context questions, or keeping costs lower than fine‑tuning a huge model. Avoid RAG when answers must come exclusively from a single authoritative source unless retrieval can be tightly constrained, or when ultra‑low latency is required and retrieval overhead is unacceptable.

Six steps in a typical RAG pipeline

1. Query analysis: normalize, expand, or translate the user query (including intent detection and query reformulation).

2. Embedding/Indexing: convert documents and the query to vector embeddings and maintain a searchable vector store (with metadata).

3. Retrieval: run a nearest‑neighbor/vector search to fetch top-k candidate passages or documents.

4. Rerank/Filter: optionally rerank or filter candidates by relevance, recency, or source trustworthiness (BM25 hybrid, cross-encoder re-ranker).

5. Context construction & prompting: assemble retrieved snippets with metadata into a concise context and craft the prompt/conditioning for the LM (include citations, instructions, and constraints).

6. Generation & validation: generate the answer from the LLM, then apply post‑processing — fact checks, hallucination detection, citation formatting, and business‑rule validation before returning output.

Stages of RAG

Three core phrases

Preprocessing — parse, embed, store.

Query/Retrieve — find relevant evidence from an external corpus.

Generate — have the language model produce a grounded response, ideally with citations and confidence checks.

CAG (Cache Augmented Generation)

CAG is an alternative to RAG (Retrieval Augmented Generation) where instead of dynamically retrieving documents at query time, you preload all relevant knowledge directly into the LLM’s context window (cache) ahead of time.

Key idea: Load your entire knowledge base into the prompt once → the model “has” all the information and can answer any question from it without a retrieval step.

MAG (Memory Augmented Generation)

Oracle’s Richmond Alake expains in a recent presentation that ‘memory is now the last battleground in the AI stack’.

Perhaps.

MAG gives models the ability to remember things across interactions. Rather than retrieving facts from an external source like RAG does, MAG recalls what’s already happened in previous conversations or tasks — the model’s own past prompts, responses, or user inputs. It’s not about fetching encyclopedic knowledge; it’s about simulating memory, continuity, and personalization.

Examples of MAG include a tutoring assistant that remembers a student’s past questions, or a product recommendation engine that recalls user preferences over time — MAG creates a sense of long-term awareness even if the underlying model itself is stateless.

HAG (Human Augmented Generation)

But as quantum systems come online, they are resolving current problems and creating new ones. Agentic tooling and decision making can now pick the tools for the job. We can begin to envision the augmentation of the retrieval stack with powerful previously unknown algorithms lights out faster than the clunky coin operated bit looms in operation today.

Our human brain, not understood by anyone, offers a clue to the technologies that will overtake AI. Quantum AI (QAI) offers a stunning performance gain — for example essentially recreating planetary environments from frequencies. Imagine recreating Earth’s moon or Europa, a moon of Jupiter, and Enceladus, a moon of Saturn, at scale on earth.

The annealing capabilities alone in quantum open a whole new dimension of investigatory capability.

The same system that built RAG, CAG and MAG will soon be available outside the human body.

Resources

Memory

https://learn.deeplearning.ai/courses/agent-memory-building-memory-aware-agents/information with Oracle’s Richmond Alake, and Nacho Martínez

https://oracle-devrel.github.io/oracle-ai-developer-hub/guides/memory_engineering_devweek_sf_2026.pdf

Memory — the Last Frontier in AI Stack?

Building a Memory-First Agent Harness with Oracle AI Database (April 2026)

On the Cutting Edge

https://medium.com/ai-in-plain-english/vector-rag-is-dead-pageindex-just-proved-it-470ea6ac446a

One Navigator. One AI. Zero Scheduling.

The Navigator and the Driver: A New Model for AI Pair Programming

Analyzing Wind on Venus with Python Audio: Soviet Venera 13 and 14 Program 1982

David Grunwald is a 32-year Oracle veteran, teacher and author. He is driven by the need to explain AI systems to improve them and avoid accidents.

© 2026 David Grunwald. All rights reserved.