Building Your First RAG Pipeline: A Developer’s Guide to Context-Aware AI

The Problem: LLMs Are Frozen in Time

If you ask GPT-4 about a library that was released last week, it will hallucinate. It has to. Its training data has a cut-off date. This is the fundamental limitation of Large Language Models (LLMs) in enterprise automation: they don't know your data, and they don't know new data.

As an automation engineer, I don't need a creative writer; I need an accurate processor of information. We solve the hallucination and currency problem not by training new models (which is expensive and slow), but by changing the architecture of how we query them.

Enter RAG (Retrieval Augmented Generation).

In this guide, we aren't just talking high-level concepts. We are going to build a functional RAG pipeline. We will look at how to ingest documents, the nuances of chunking data, vector storage, and finally, how to evaluate if your system is actually telling the truth.

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The Architecture: How RAG Works

RAG is essentially an open-book exam for AI. Instead of forcing the model to memorize facts (weights), we give it the textbook (context) at the moment of inference.

The architecture consists of three distinct phases:

1. Ingestion & Indexing: Turning unstructured data (PDFs, docs, databases) into mathematical vectors.
2. Retrieval: Finding the most relevant pieces of information based on a user query.
3. Generation: Passing that retrieved context to the LLM to synthesize an answer.

Let's build this stack. For this demo, we will use LangChain for orchestration, ChromaDB as our vector store, and OpenAI for embeddings and generation. This is the standard "modern data stack" for AI capability.

Prerequisites

You'll need a Python environment with the following libraries:

pip install langchain langchain-openai chromadb tiktoken pypdf

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Phase 1: Ingestion and The Art of Chunking

Most beginners fail at RAG because they dump entire documents into the database. LLMs have context windows, and vector search relies on semantic similarity. If a chunk is too large, the specific needle of information gets lost in the haystack.

We need to break our document into chunks. But how you slice matters.

Recursive Character Text Splitting

We want to keep related text together. A sentence shouldn't be cut in half. A paragraph is usually a coherent unit of thought.

from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter

# 1. Load the Data
loader = PyPDFLoader("./technical_manual.pdf")
docs = loader.load()

# 2. Chunk the Data
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,      # Characters per chunk
    chunk_overlap=200,    # Overlap to maintain context between breaks
    add_start_index=True
)

all_splits = text_splitter.split_documents(docs)
print(f"Split into {len(all_splits)} chunks.")

Builder Note: The chunk_overlap is critical. Without it, if a crucial sentence is split between Chunk A and Chunk B, the semantic meaning is severed. The overlap ensures the "connective tissue" of the context remains intact.

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Phase 2: Embeddings and Vector Storage

Machines don't understand text; they understand numbers. To search our documents, we convert text chunks into Vectors (long lists of floating-point numbers). Concepts that are semantically similar will be mathematically close to each other in vector space.

We will use ChromaDB, a local, open-source vector store. It’s lightweight and perfect for micro-SaaS or internal tools.

from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings

# Initialize Embedding Model
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")

# Create Vector Store
vectorstore = Chroma.from_documents(
    documents=all_splits,
    embedding=embeddings,
    persist_directory="./chroma_db"
)

# Configure Retriever
retriever = vectorstore.as_retriever(
    search_type="similarity",
    search_kwargs={"k": 4} # Retrieve the top 4 relevant chunks
)

When you run this, your text is passed to OpenAI, converted to vectors, and stored locally in Chroma. The k=4 argument tells the system: "When asked a question, find the 4 most relevant chunks of text."

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Phase 3: The Retrieval Chain

Now we have the data. We need to wire it to the LLM. We are creating a chain that follows this logic:

1. Take User Question.
2. Hit Vector Store $\to$ Get Context.
3. Combine [Question + Context] into a prompt.
4. Send to LLM $\to$ Get Answer.

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI

# Initialize LLM
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

# Define the RAG Prompt
rag_template = """
You are a technical assistant. Use the following context to answer the question.
If you don't know the answer, say you don't know. Do not make up info.

Context:
{context}

Question:
{question}
"""

prompt = ChatPromptTemplate.from_template(rag_template)

# Helper function to format docs
def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

# Build the Chain
rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

# Run it
response = rag_chain.invoke("How do I reset the configuration parameters?")
print(response)

Why `temperature=0`?

In creative writing, we want high temperature (randomness). In RAG, we want deterministic output. We want the model to rely only on the retrieved context, not its internal training creativity.

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The "So What?": Evaluation

You built the pipeline. It gives an answer. But is it the right answer?

In a production automation environment, blindly trusting the output is dangerous. This is where RAG Evaluation comes in. We look at two main metrics:

1. Retrieval Metrics (Hit Rate): Did we actually find the right document? If the answer is in the PDF but the retriever didn't pass it to the LLM, the system failed before generation even started.
2. Generation Metrics (Faithfulness): Did the LLM answer based on the context provided, or did it hallucinate?

Tools like Ragas or TruLens are essential here. A simple evaluation loop looks like this conceptually:

• Context Precision: Is the retrieved context relevant to the query?
• Context Recall: Is the retrieved context sufficient to answer the query?
• Faithfulness: Is the answer derived purely from the context?

If your RAG system feels "dumb," 90% of the time the issue is in the Retrieval step (bad chunking or bad search), not the Generation step.

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Optimization: Going Beyond Basics

The code above is the "Hello World" of RAG. It works for simple docs. But when you scale to thousands of files or complex technical manuals, simple similarity search fails.

To make this production-ready, consider these upgrades:

• Hybrid Search: Combine Vector search (semantic) with Keyword search (BM25). This catches specific acronyms or serial numbers that vector search misses.
• Metadata Filtering: Filter chunks by date, author, or file type before searching.
• Re-ranking: Retrieve 20 documents, then use a specialized re-ranking model (like Cohere) to sort the top 5 most relevant ones before sending them to the LLM.

Conclusion

RAG is the backbone of modern AI automation. It allows us to build agents that are grounded in reality and specific to our business data. Start with the pipeline above. Get the ingestion working. Then, spend your time tuning the chunk size and overlap—that is where the battle for accuracy is won.

Now, go build something.