Start Lesson
I have built RAG systems that served millions of queries. I have also built RAG systems that fell apart the moment real users touched them. The difference was never the model or the vector database. It was always the engineering between the pieces.
Most RAG tutorials skip the hard part. They show you a five-line LangChain script that retrieves documents and stuffs them into a prompt. It works on a curated dataset of 50 documents. Then you deploy it against 500,000 documents with messy formatting, conflicting information, and users who ask questions nothing like your test set. Everything breaks.
This lesson maps the failure modes I see repeatedly so you can build guardrails against them from day one.
Here is what a typical RAG demo looks like:
User query -> Embed query -> Vector search (top-5) -> Stuff into prompt -> LLM generates answer
Here is what a production RAG system actually requires:
User query
-> Query understanding & rewriting
-> Hybrid retrieval (dense + sparse)
-> Re-ranking & filtering
-> Context window management
-> Citation extraction
-> Answer generation with guardrails
-> Quality monitoring & logging
-> Cost tracking per query
The demo has 4 steps. Production has 9+. Every missing step is a failure mode.
This is the most common failure and the hardest to diagnose. Your system retrieves something --- just not the right something. The LLM confidently generates an answer from irrelevant context, and the user has no way to tell.
Root causes:
The fix: Measure retrieval recall and precision separately from answer quality. I will cover this in Lesson 8.7.
You retrieve 10 chunks, concatenate them, and stuff them into the prompt. But three of those chunks contradict each other. Two are duplicates. One is from 2019 and outdated. The LLM now has to navigate conflicting information with no guidance about which source to trust.
Root causes:
The fix: Treat the context window like expensive real estate. Every token you send to the LLM costs money and adds noise. Re-rank aggressively, deduplicate, and limit to the minimum viable context.
I once debugged a system where the answer to "What is the refund policy?" was split across three chunks --- the conditions in one, the timeline in another, and the exceptions in a third. The retriever found one chunk, generated a partial answer, and the user got wrong information.
Root causes:
The fix: Chunking is not a preprocessing step you set and forget. It is a core architectural decision. Lesson 8.2 covers this in depth.
A single RAG query in a naive pipeline involves: embedding the query, searching the vector database, embedding retrieved chunks (if re-ranking), and sending a large context to the LLM. At scale, this adds up fast.
I have seen teams burning $50,000/month on a RAG system that served 100,000 daily queries --- most of which were near-duplicates. No caching. No query deduplication. No model routing. Just raw, unoptimized inference at every step.
Root causes:
The fix: Think of RAG as an information supply chain with unit economics. Every query has a cost, and every cost has a lever. Lesson 8.5 covers the playbook I used to cut retrieval costs by 99%.
When a user reports "the AI gave me a wrong answer," you need to reconstruct exactly what happened: what query was sent, what was retrieved, what context was assembled, and what the LLM generated. Without tracing, you are debugging blind.
Root causes:
The fix: Instrument every stage. I cover the full observability stack in Lesson 8.8.
I think about RAG as an information supply chain. Raw documents are your raw materials. Chunking is manufacturing. Embeddings are packaging. The vector database is your warehouse. Retrieval is the logistics network. The LLM is the assembly line that produces the final product.
When you think this way, the optimization levers become clear:
| Supply Chain Stage | RAG Equivalent | Key Metric | |--------------------|----------------|------------| | Raw materials | Document ingestion | Coverage, freshness | | Manufacturing | Chunking | Information density per chunk | | Packaging | Embedding | Semantic fidelity | | Warehousing | Vector storage | Cost per vector, query latency | | Logistics | Retrieval | Recall, precision, latency | | Assembly | LLM generation | Faithfulness, cost per query | | Quality control | Evaluation | End-to-end accuracy |
Every stage has failure modes, cost drivers, and optimization opportunities. This course walks through each one.
When something goes wrong, you need a systematic way to identify which failure mode you are hitting. Here is the diagnostic sequence I use:
1. Pull a sample of 20 "bad" answers from user feedback or quality audits
2. For each bad answer, run the query through retrieval only (no LLM)
3. Manually check: did the retriever find the right document?
- NO -> Failure Mode 1 (retrieval miss) or 3 (chunking)
- YES -> Continue
4. Check: was the right document ranked in the top 3?
- NO -> Context window problem (Failure Mode 2)
- YES -> Continue
5. Check: did the LLM use the right information from context?
- NO -> Generation/prompt problem
- YES -> The answer may actually be correct; re-examine the user's expectation
In my experience, roughly 70% of "bad answers" trace back to retrieval (steps 3-4). Only about 20% are generation problems. The remaining 10% are ambiguous queries where the user's intent was unclear. This is why measuring retrieval quality independently is the single highest-leverage debugging step.
Use this checklist to assess where your RAG system stands today:
If you checked fewer than 3, your system has significant production gaps. This course addresses every unchecked item.
We dig into the first critical stage of the supply chain: chunking strategies that actually work. The wrong chunking decision creates failure modes 1 and 3 from this lesson, and no downstream fix can compensate.