Why Your Simple RAG Is Failing (And What to Do About It)

In our previous projects, we built a RAG (Retrieval-Augmented Generation) pipeline. It follows a simple, linear checklist:

1. Retrieve documents.
2. Stuff them into a prompt.
3. Generate an answer.

This "checklist" pattern is great for simple questions. But this post is for you if you've ever built a RAG bot and been disappointed when it fails on a complex, real-world query.

Today, we'll demonstrate why this simple RAG pipeline is brittle and set the stage for a "smarter" solution.

The problem: The "Brittle Checklist"

A simple RAG pipeline is like a diligent intern who only follows their checklist.

You ask the intern, "How does our new product compare to our main competitor's?" The intern checks your internal documents, finds nothing about the competitor, and replies, "I don't have that information."

They're technically correct, but they're not helpful. They didn't recognize the gap, decide to look elsewhere (like Google), or synthesize a complete answer.

This is the failure of simple RAG. It has no "decision-making" capability.

The "Failure Case": A live test

Let's prove this failure. We'll build a knowledge base with info about our (fictional) product, "Model-V," but nothing about its competitors.

The "How": We'll set up a simple ChromaDB vector store.

import chromadb
from sentence_transformers import SentenceTransformer

# 1. Our "internal-only" documents
documents = [
    "The Model-V is our latest innovation in AI, featuring a 5-trillion parameter architecture.",
    "Built on a unique 'Quantum Entanglement' processing core, the Model-V achieves unprecedented speeds.",
    "Model-V's training data includes a proprietary dataset of scientific research papers."
]

# 2. Set up the vector store
client = chromadb.Client()
collection = client.get_or_create_collection(name="product_docs")
collection.add(
    documents=documents,
    ids=[str(i) for i in range(len(documents))]
)

Observation: Our vector store is now "live" and contains three facts only about Model-V.

Now, let's run our "brittle" RAG pipeline against it with a question it cannot answer.

graph TD
    A["User Query: 'Model-V vs. Model-Z?'"] --> B(1. Retrieve)
    B --> C[Internal Docs about Model-V]
    C --> D(2. Augment Prompt)
    D --> E(3. Generate)
    E --> F[Useless Answer]
    
    style F fill:#ffebee,stroke:#b71c1c,color:#212121

The "How": A standard RAG function.

from openai import OpenAI

llm_client = OpenAI() # Assumes OPENAI_API_KEY is set

# This is our "dumb intern" RAG
def simple_rag_pipeline(query):
    # 1. Retrieve (finds docs about Model-V)
    retrieved_docs = collection.query(query_texts=[query], n_results=2)['documents'][0]
    context = "\n".join(retrieved_docs)
    
    # 2. Augment & Generate
    basic_rag_prompt = [
        {"role": "system", "content": "Answer the user's question based ONLY on the provided context."},
        {"role": "user", "content": f"Context:\n{context}\n\nQuestion:\n{query}"}
    ]
    
    response = llm_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=basic_rag_prompt,
        temperature=0
    ).choices[0].message.content
    
    return response

# 3. The Failing Query
query = "How does the Model-V compare to the new Model-Z from our competitor?"

print(simple_rag_pipeline(query))

The (Failed) Result:

Based on the provided context, the Model-V has a 5-trillion parameter
architecture and a 'Quantum Entanglement' processing core. The context
does not provide any information about a 'Model-Z' or any competitors.

Observation: Our bot failed completely. It followed its "checklist" (find docs, generate answer) perfectly, but it wasn't smart enough to realize the checklist itself was flawed. It didn't know it needed to "look elsewhere."

This is the fundamental limit of simple RAG.

Think About It: Besides competitor comparisons, what other types of user questions would cause this simple RAG system to fail? (Hint: Think about recent events, or questions that require multiple steps.)

The solution (Our next step)

To fix this, we need to move from a "linear checklist" to a "dynamic, thinking process." We need to build a system that can:

1. Route tasks to the right tool.
2. Grade its own work.
3. Correct its mistakes.

In our next post, we'll introduce LangGraph and start building the "brain" for our new, intelligent agent.

Key takeaways

• Simple RAG follows a rigid checklist: Retrieve, augment, generate—this works for straightforward queries but fails on complex questions
• The failure is in decision-making: Simple RAG can't recognize when it needs to look elsewhere or use different tools
• Complex queries expose the weakness: Questions about competitors, recent events, or multi-step reasoning break the linear pipeline
• We need dynamic routing: The system must decide which tool to use (internal docs vs. web search) based on the query
• Self-correction is essential: The system must be able to grade its own results and try alternative approaches when it fails

For more on RAG fundamentals, see our RAG introduction. For advanced RAG patterns, check out our self-correcting RAG systems guide.

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