Building a RAG System for Technical Documentation: A Practical Guide

Your company has hundreds of pages of technical documentation. Your engineers need answers fast. Traditional search returns keywords, not answers. RAG (Retrieval-Augmented Generation) changes that—turning static documents into an intelligent assistant that actually understands questions.

Here's how it works and how to build one.

What is RAG and Why Should You Care?

RAG stands for Retrieval-Augmented Generation. In plain English: instead of asking an AI to answer from its general knowledge (which may be outdated or wrong), you first retrieve relevant information from your own documents, then ask the AI to answer based on that specific context.

The result? An AI assistant that:

• Answers questions using your actual documentation
• Provides sources for every answer
• Doesn't hallucinate facts it doesn't have
• Stays current when you update your documents

For technical documentation, this is transformative. Engineers ask questions in natural language and get precise answers with references—in seconds instead of hours.

The Architecture: Four Building Blocks

A RAG system has four main components:

Documents ──▶ Chunking ──▶ Embeddings ──▶ Vector DB
  (PDFs)      & Processing   (OpenAI)     (Weaviate)
                                              │
                                              ▼
  Answer  ◀──    LLM     ◀──  Retrieved  ◀── Query
                (OpenAI)      Context

Let's break down each component.

1. Document Processing & Chunking

Raw PDFs aren't useful to an AI. You need to extract the text and split it into manageable chunks—small enough to be relevant, large enough to contain meaningful context.

from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Load a PDF
loader = PyPDFLoader("technical_manual.pdf")
pages = loader.load()

# Split into chunks
splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,  # Overlap prevents cutting sentences mid-thought
    separators=["\n\n", "\n", ". ", " ", ""]
)

chunks = splitter.split_documents(pages)
print(f"Created {len(chunks)} chunks from document")

Why chunk overlap matters: Technical procedures often span multiple paragraphs. Overlap ensures you don't lose context at chunk boundaries.

2. Creating Embeddings

Embeddings convert text into numerical vectors that capture semantic meaning. Similar concepts end up close together in vector space—so "maximum operating pressure" and "pressure limits" are recognized as related, even without keyword matching.

from openai import OpenAI

client = OpenAI()

def get_embedding(text: str) -> list[float]:
    """Convert text to a vector embedding."""
    response = client.embeddings.create(
        model="text-embedding-3-small",
        input=text
    )
    return response.data[0].embedding

# Example
embedding = get_embedding("What is the maximum pressure rating?")
print(f"Embedding dimension: {len(embedding)}")  # 1536 dimensions

3. Storing in a Vector Database

A vector database like Weaviate stores your embeddings and enables fast similarity search. When a user asks a question, you convert it to an embedding and find the most similar document chunks.

import weaviate

# Connect to Weaviate
client = weaviate.Client("http://localhost:8080")

# Create a schema for your documents
schema = {
    "class": "TechnicalDocument",
    "vectorizer": "none",  # We provide our own embeddings
    "properties": [
        {"name": "content", "dataType": ["text"]},
        {"name": "source", "dataType": ["string"]},
        {"name": "page", "dataType": ["int"]}
    ]
}

client.schema.create_class(schema)

# Index a chunk
client.data_object.create(
    class_name="TechnicalDocument",
    data_object={
        "content": chunk.page_content,
        "source": chunk.metadata["source"],
        "page": chunk.metadata["page"]
    },
    vector=get_embedding(chunk.page_content)
)

4. Retrieval and Generation

When a user asks a question, you:

1. Convert the question to an embedding
2. Find the most similar chunks in your vector database
3. Pass those chunks as context to an LLM
4. Get an answer grounded in your documentation

def ask_question(question: str, top_k: int = 5) -> str:
    """Ask a question and get an answer from your documentation."""

    # Step 1: Embed the question
    question_embedding = get_embedding(question)

    # Step 2: Find relevant chunks
    results = client.query.get(
        "TechnicalDocument",
        ["content", "source", "page"]
    ).with_near_vector({
        "vector": question_embedding
    }).with_limit(top_k).do()

    # Step 3: Build context from retrieved chunks
    chunks = results["data"]["Get"]["TechnicalDocument"]
    context = "\n\n".join([chunk["content"] for chunk in chunks])

    # Step 4: Generate answer with LLM
    response = openai_client.chat.completions.create(
        model="gpt-4",
        messages=[
            {
                "role": "system",
                "content": """You are a technical assistant. Answer questions 
                based only on the provided context. If the context doesn't 
                contain the answer, say so. Always cite the source document 
                and page number."""
            },
            {
                "role": "user",
                "content": f"Context:\n{context}\n\nQuestion: {question}"
            }
        ]
    )

    return response.choices[0].message.content

# Example usage
answer = ask_question("What is the maximum operating pressure for the X-500 valve?")
print(answer)

Common Mistakes to Avoid

After building several RAG systems, here are the pitfalls I see most often:

1. Chunks Too Large or Too Small

• Too large: Retrieved context includes irrelevant information, confusing the LLM
• Too small: Context lacks necessary details for a complete answer

Solution: Start with 1000 characters with 200 overlap. Adjust based on your document structure.

2. Ignoring Document Structure

Technical manuals have sections, headers, and tables. Naive chunking ignores this structure and creates chunks that cut across logical boundaries.

Solution: Use document-aware chunking that respects headers and sections when possible.

3. No Source Attribution

Users need to verify answers. A RAG system without source citations is just a black box.

Solution: Always return the source document and page number with every answer.

4. Skipping Evaluation

How do you know your RAG system works? Many teams deploy without measuring accuracy.

Solution: Create a test set of questions with known answers. Measure retrieval accuracy and answer quality before deployment.

When Does RAG Make Sense?

RAG is powerful but not always necessary. It's the right choice when:

• You have substantial documentation (manuals, procedures, standards)
• Information needs are specific and varied
• Documentation changes over time
• Users need answers, not document lists

It might be overkill if:

• You have just a few small documents
• Simple keyword search already works well
• Questions are always the same (a simple FAQ might suffice)

The Business Impact

A well-built RAG system transforms how teams interact with documentation:

Metric	Before RAG	After RAG
Time to find information	Hours to days	Seconds
New employee onboarding	Months to learn documentation	Immediate access
Knowledge retention	Leaves with people	Stays in the system
Answer consistency	Varies by who you ask	Consistent, sourced

For technical organizations with extensive documentation, the ROI is substantial—not in abstract "efficiency gains" but in real hours saved and better decisions made.

Next Steps

If you're considering building a RAG system for your organization:

1. Audit your documentation: What do you have? Where is it? What format?
2. Identify high-value use cases: What questions do people ask most often?
3. Start small: Pilot with one document set before scaling
4. Measure baseline: How long do searches take today?
5. Iterate: RAG systems improve with tuning and feedback

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Want to explore whether a RAG system makes sense for your technical documentation? Book a free strategy call and let's discuss your situation.