Interactive guide
Retrieval-Augmented
Generation (RAG)
LLMs are powerful but blind to your private docs and yesterday's deploy. RAG retrieves relevant passages at query time, stuffs them into the prompt, and grounds the answer — without retraining weights.
Three parts: The gap → The pipeline → Ship it. Step 04 is the full retrieve → generate playground.
Part 1 · Step 01
The gap RAG fills
Start with how LLM chat works today, name what's missing, then walk the full RAG data flow — indexing offline, retrieval and generation at query time.
Query time only — no document path
This is the default "chat with GPT" architecture — powerful language skill, but no live wire into your knowledge base.
Same question, two architectures — toggle a sample query below:
User: How many PTO days do employees get?
Without RAG
Most companies offer around 10-15 vacation days, but policies vary widely.
With RAG
Employees receive 20 days of PTO per year, with up to 5 days rollover. New hires accrue from day one.
Part 2 · Step 02
The RAG pipeline
Two phases: indexing (offline) builds a searchable memory; query time retrieves, augments, and generates.
Click each stage — this is the query-time half of the RAG data flow you saw in Step 01. Indexing (offline) happens separately.
Documents are chunked, embedded, and stored before any user asks a question.
Part 2 · Step 03
Indexing: chunk, embed, store
Documents are split into chunks, converted to vectors, and stored in a vector database. This is the knowledge base your app queries — not the LLM weights.
Acme Analytics employees receive 20 days of PTO per year.
Unused PTO rolls over up to 5 days.
Remote work is allowed up to 3 days per week with manager approval.
New hires accrue PTO starting day one.
Indexing runs offline: split text, embed each chunk, upsert into a vector database (Pinecone, pgvector, OpenSearch, etc.).
Part 2 · Step 04 — interactive
Retrieve, augment, generate
Watch similarity scores pick chunks, see the augmented prompt, and read the grounded answer. This is what most "chat with your docs" products implement under the hood.
Query: How many PTO days do employees get?
Retrieved chunks
Acme Analytics employees receive 20 days of PTO per year.
Unused PTO rolls over up to 5 days.
Augmented prompt
System: Answer using ONLY the context below. If the answer is not in the context, say you don't know. Context: [1] (HR Policy) Acme Analytics employees receive 20 days of PTO per year. [2] (HR Policy) Unused PTO rolls over up to 5 days. User: How many PTO days do employees get? Assistant:
Generated answer
Employees receive 20 days of PTO per year, with up to 5 days rollover. New hires accrue from day one.
Part 3 · Step 05
Chunking tradeoffs
Chunk size shapes retrieval quality. Too large adds noise; too small loses context. Production systems often use 256–512 tokens with overlap.
Query: What is the API rate limit per minute?
15 chunks in index · top match scores below
The rate limit is 1000 requests per minute per API key.
Escalate to the platform team if error rate exceeds 2%.
Small chunks improve precision — the rate-limit sentence ranks high without extra noise.
Part 3 · Step 06
Pitfalls & architecture choices
RAG shifts the problem from "can the model answer?" to "did we fetch the right evidence?" — plus classic LLM security and freshness concerns.
Bad retrieval = bad answers
If the right chunk isn't retrieved, the LLM cannot magically know your policy. Monitor recall@k and human eval on retrieval.
Mitigation: Better chunking, hybrid search (BM25 + vectors), rerankers
Engineering checklist: chunk strategy, embedding model, top-k, prompt template, access control on the vector store, logging of retrieved chunks for debugging, and eval sets with real internal questions.