This is for our Infra Admins who would be more interested in understanding RAG (Retrieval-Augmented Generation) with Infra lens.
RAG Framework — Retriever, Ranker, LLM
Think of this as a three-tier help-desk / ticketing search system:
Retriever — sources relevant information from a large corpus or database using retrieval techniques. This is your search index across your runbooks, KB articles, and past ticket resolutions — like querying your AD/VMware documentation repository for anything that might match the current issue. It casts a wide net, pulling anything plausibly relevant.
Ranker — evaluates and prioritizes what the retriever pulled back, ensuring the LLM gets the most pertinent, high-quality input. This is exactly like triaging search results by relevance before escalating — you don't hand a level-1 tech every KB article that mentions "DNS," you rank by how closely each one matches the actual symptom.
LLM — generates the final human-like response using the ranked, retrieved information plus the original query, aiming for a response that's factually accurate, coherent, and grounded. This is your senior engineer writing up the final resolution note, synthesizing the best-ranked reference material into a clear answer — not inventing one from memory.
RAG Pipeline — the three stages that make this run (Ingestion → Retrieval → Generation): Ingestion is your one-time (or scheduled) documentation import job — documents get chunked, embedded, and indexed into a database, much like you'd batch-import and index a KB into a searchable system. Retrieval is the live query time lookup — a user's question gets matched against that index to pull top-K results. Generation is the final answer synthesis — those top results get handed to the LLM to produce the response.
RAG Techniques — RAG Sequence vs. RAG Token
RAG Sequence: retrieve documents once for the whole query, then generate one cohesive response using all of them together. Like pulling every relevant runbook for an incident up front, then writing one complete resolution report referencing all of them together.
RAG Token: retrieve fresh documents for each part of the response as it's being generated, building the answer incrementally. Like looking something up mid-sentence while writing a report — checking a different reference for each paragraph as you go, rather than gathering everything first. More flexible, but more overhead per response.
Types of RAG — Naive, Advanced, Agentic (this is really a maturity ladder, and it maps almost exactly onto how monitoring/automation tooling matures in an infra team):
Naive RAG — embed the query, grab the top-K matches once, stuff them in the prompt, generate. Simple but brittle if the first search misses. This is like a basic keyword search against your KB with no re-checking — if the search terms don't match well, you get a bad answer with no safety net.
Advanced RAG — adds query rewriting, re-ranking, and better chunking before generation. This is like adding synonym matching, tagging, and a relevance-scoring layer to your ticketing search — you're improving the quality of what gets pulled before anyone acts on it.
Agentic RAG — the LLM itself plans its own retrieval: deciding what to search for, issuing multiple/iterative queries, calling tools, and judging whether it has enough context before answering. This is the leap from a static runbook lookup to a self-driving diagnostic agent — like a monitoring system that doesn't just alert once but iteratively checks multiple data sources (event logs, performance counters, AD replication status) on its own before concluding root cause.
One practical connection if you ever build a "chatbot over your AD/VMware runbooks" idea from earlier, you'd start with Naive RAG to prove it works, then move to Advanced RAG once you notice bad retrievals (mismatched KB articles), and only reach for Agentic RAG if the questions genuinely require multi-step investigation rather than a single lookup — same incremental-rollout instinct you'd apply to any new tooling in production.