RAG Pipeline Tutorial: Your Documents to AI Chatbot

Jul 17, 2025· PolicyChatbot Team

You know what’s funny?

Everyone talks about RAG like it’s some mystical technology that only ML engineers at OpenAI understand. Meanwhile, I’m watching junior developers build RAG systems in hackathons using YouTube tutorials.

The truth? RAG isn’t complicated. The implementation is complicated. There’s a difference.

It’s like cooking. Anyone can understand that pasta + sauce = dinner. But making fresh pasta from scratch while simultaneously preparing a béchamel sauce and not burning your kitchen down? That’s where things get interesting.

So let’s talk RAG. Real talk. Not the “here’s the theoretical framework” talk, but the “here’s what actually happens when you try to build this thing” talk.

What RAG Actually Is (In Plain English)

RAG stands for Retrieval-Augmented Generation. Terrible name. Here’s what it actually means:

Instead of asking an AI to answer from its training data (which might be outdated or wrong), you:

Find relevant information from your documents
Give that information to the AI
Let the AI generate an answer using that specific information

It’s like the difference between asking someone to recall something from memory versus giving them the relevant Wikipedia page and asking them to summarize it.

Simple concept. Devil’s in the implementation details.

The Journey of a Question

Let me walk you through what actually happens when someone asks your chatbot a question. We’ll follow a simple query: “What’s our remote work policy?”

Step 1: The Question Enters the Arena

User types: “What’s our remote work policy?”

Seems simple enough. But immediately, we have problems:

Is this question even about company policies?
Which documents might contain the answer?
What if the policy is spread across multiple documents?
What if they meant “work from home” instead of “remote work”?

This is where most DIY implementations fall apart. They skip straight to search without considering these questions.

Step 2: The Relevance Check

Before we waste expensive compute resources, we need to check: is this question even relevant to our knowledge base?

def check_relevance(question, knowledge_domain):
    # Most people skip this step. Don't be most people.
    prompt = f"""
    Is this question related to {knowledge_domain}?
    Question: {question}
    Answer with just 'yes' or 'no'.
    """
    
    response = cheap_model.complete(prompt)
    return response.lower() == 'yes'

Why does this matter? Because when someone asks “What’s the weather today?” to your policy chatbot, you don’t want to waste time searching through HR documents. You want to immediately respond with “I can only answer questions about company policies.”

PolicyChatbot does this automatically. Building it yourself? Add two weeks to your timeline.

Step 3: The Embedding Dance

Now here’s where things get technical…

Your question needs to be converted into numbers. Not just any numbers – a 1024-dimensional vector that represents the meaning of the question.

# What people think happens:
embedding = embed_text("What's our remote work policy?")

# What actually happens:
try:
    # Tokenize the text
    tokens = tokenizer.encode(question)
    
    # Check token limits
    if len(tokens) > 8192:
        tokens = tokens[:8192]  # Truncate
    
    # Call the embedding API
    response = voyage_ai.embed(
        texts=[question],
        model="voyage-3",
        input_type="query"  # Different from document!
    )
    
    # Handle the response
    embedding = response['embeddings'][0]
    
except RateLimitError:
    # Wait and retry
    time.sleep(exponential_backoff())
    # Try again...
    
except APIError:
    # Fall back to cached embeddings? 
    # Use a different model?
    # Cry?

Notice the input_type="query" parameter? Yeah, turns out query embeddings and document embeddings should be generated differently. Found that out the hard way after three weeks of terrible search results.

Step 4: The Vector Search

Now we search. But not like Google search. This is semantic search.

Your question’s embedding gets compared to thousands of document chunk embeddings using cosine similarity. Sounds fancy. It’s basically asking: “Which document chunks point in the same direction in 1024-dimensional space?”

# The naive approach:
results = []
for chunk in all_chunks:
    similarity = cosine_similarity(query_embedding, chunk.embedding)
    results.append((chunk, similarity))

results.sort(key=lambda x: x[1], reverse=True)
top_chunks = results[:20]

This works for 100 documents. For 10,000 documents? Your server catches fire.

Real implementation needs:

Vector indexes (HNSW, IVF, etc.)
Approximate nearest neighbor search
Metadata filtering
Hybrid search (combining with keyword search)

PolicyChatbot uses pgvector with optimized indexes. Setting this up yourself? That’s another week gone.

Step 5: The Reranking Revolution

Here’s what most tutorials don’t tell you: vector search results are often garbage.

Not completely garbage. But the 3rd result might be more relevant than the 1st. The 15th might be crucial. Vector similarity doesn’t equal relevance.

Enter reranking:

# What you need to do:
def rerank_chunks(question, chunks):
    # Voyage AI reranking (the good stuff)
    reranked = voyage_ai.rerank(
        query=question,
        documents=[chunk.text for chunk in chunks],
        model="rerank-2"
    )
    
    # Reorder chunks based on relevance scores
    return [chunks[r.index] for r in reranked.results]

But wait! Reranking APIs have limits:

Max documents per request (usually 100)
Rate limits (again)
Cost (reranking isn’t free)
Latency (adds 200-500ms)

So now you need:

Batching logic
Fallback strategies
Caching mechanisms
Performance monitoring

Starting to see why this takes months to build?

Step 6: The Context Window Tetris

You’ve got your reranked chunks. Time to stuff them into the LLM’s context window.

But wait… context windows have limits:

GPT-4: 128k tokens
Claude: 200k tokens
Your budget: Way less than that

Each token costs money. So you need to be smart:

def build_context(chunks, max_tokens=4000):
    context = []
    token_count = 0
    
    for chunk in chunks:
        chunk_tokens = count_tokens(chunk.text)
        
        if token_count + chunk_tokens > max_tokens:
            break
            
        context.append(chunk.text)
        token_count += chunk_tokens
    
    return "\n\n".join(context)

But this is too simple. Real implementation needs:

Token counting that matches the model’s tokenizer
Smart truncation (don’t cut mid-sentence)
Deduplication (remove redundant information)
Context ordering (most relevant first? chronological?)
Source attribution (which document did this come from?)

Step 7: The Prompt Engineering Nightmare

Now you need to tell the LLM what to do with this context.

prompt = f"""
Use the following context to answer the question.
If the answer is not in the context, say "I don't know."

Context:
{context}

Question: {question}

Answer:
"""

Haha, no. That prompt will give you hallucinations, made-up facts, and responses that sound like a robot having an existential crisis.

Real prompt needs:

Role definition
Explicit constraints
Output format specifications
Example responses
Fallback behaviors
Tone guidelines
Citation requirements

Here’s what actually works:

prompt = f"""
You are a helpful assistant answering questions about company policies.

IMPORTANT RULES:
1. Only use information from the provided context
2. If the answer isn't in the context, say "I couldn't find that information in our policies"
3. Be concise but complete
4. Use bullet points for lists
5. Quote directly when referring to specific policies
6. Maintain a professional but friendly tone

Context from company documents:
{context}

Employee Question: {question}

Provide a clear, accurate answer based solely on the context above.
If you need to reference a specific policy, quote it directly.
"""

And this is still basic. Production prompts are often 500+ lines with complex logic.

Step 8: The Response Generation

Finally! We can generate a response:

response = llm.complete(
    prompt=prompt,
    temperature=0.3,  # Lower = more consistent
    max_tokens=500,   # Prevent rambling
    stop_sequences=["Employee Question:", "Context:"],  # Don't leak prompt
)

But of course, things go wrong:

Token limits exceeded
Rate limits (yes, again)
Timeout errors
Content filtering triggers
Incomplete responses
JSON parsing errors (if you want structured output)

Each failure needs handling. Each handling needs testing. Each test finds new edge cases.

The Real Implementation Challenge

Let me show you what building a production RAG pipeline actually looks like:

Month 1: The Honeymoon Phase

Week 1: “We’ll use LangChain!” Week 2: “LangChain is too abstract, let’s use raw APIs” Week 3: “These APIs are unreliable, let’s add retry logic” Week 4: “Why is everything so slow?”

Month 2: The Reality Check

Week 5: Discover embeddings are wrong. Start over. Week 6: Realize chunking strategy is terrible. Refactor. Week 7: Find out about query vs document embeddings. Re-embed everything. Week 8: Performance optimization. Still slow.

Month 3: The Desperation Phase

Week 9: Add caching. Breaks real-time updates. Week 10: Implement hybrid search. Results get worse. Week 11: Try different reranking. Costs explode. Week 12: “Maybe we should use a vendor solution…”

Why PolicyChatbot’s RAG Pipeline Actually Works

After watching dozens of companies go through this pain, here’s what PolicyChatbot does differently:

Smart Chunking That Makes Sense

Instead of naive splitting:

# Bad: Most DIY implementations
chunks = text.split('\n\n')[:chunk_size]

# Good: PolicyChatbot approach
chunks = intelligent_chunker(
    text,
    method='sentence',  # or 'token' or 'recursive'
    max_tokens=512,
    overlap=128,
    preserve_structure=True,  # Keep headers with content
    smart_boundaries=True     # Don't split mid-concept
)

The difference? Night and day. Proper chunking improves accuracy by 40%.

Multi-Stage Retrieval

Not just vector search:

Initial Retrieval: Get 100 candidates using vector search
Metadata Filtering: Filter by document type, date, department
Hybrid Scoring: Combine vector similarity with BM25 keyword scores
Reranking: Use Voyage AI to reorder top candidates
Diversity Sampling: Ensure chunks from different documents

This approach catches relevant information that pure vector search misses.

Adaptive Context Building

PolicyChatbot dynamically adjusts context based on:

Question complexity
Available token budget
Document relevance scores
User feedback history

Simple question? Use fewer chunks, save money. Complex question? Load more context, ensure accuracy.

Feedback-Driven Optimization

Every thumbs up/down teaches the system:

Which chunks were actually helpful
Which reranking strategies work
What context sizes are optimal
When to admit “I don’t know”

Your DIY system? It makes the same mistakes forever.

The 10-Minute Setup vs 3-Month Build

Let’s be real about what you’re choosing between:

DIY RAG Pipeline: The 3-Month Journey

Month 1:

Set up development environment
Research embedding models
Build basic vector storage
Implement simple search
Create primitive UI

Status: Barely functional demo

Month 2:

Add reranking
Improve prompts
Handle edge cases
Optimize performance
Add monitoring

Status: Works on your machine

Month 3:

Scale for production
Add security
Implement analytics
Fix all the bugs
Deploy and pray

Status: Mostly works, sometimes

Total Investment:

3 developers × 3 months = £90,000
Infrastructure costs = £5,000
API costs during development = £2,000
Stress-related therapy = Priceless

PolicyChatbot: The 10-Minute Setup

Minute 1-2: Sign up Minute 3-5: Upload documents Minute 6-7: Configure settings Minute 8-9: Test responses Minute 10: Share with team

Status: Production-ready

Total Investment:

Time: 10 minutes
Cost: £99/month
Stress level: Zero

The Features You’ll Never Build Yourself

Intelligent Fallbacks

When PolicyChatbot can’t find an answer, it doesn’t just say “I don’t know.” It:

Suggests related topics it CAN answer
Offers to search for similar questions
Provides contact information for human help
Logs the gap for content improvement

Your DIY version: “Error: No relevant chunks found”

Multi-Language Support

PolicyChatbot handles:

Questions in any language
Documents in mixed languages
Responses in the user’s language
Cross-language retrieval

Building this yourself? Add another 6 months.

Version Control for RAG

PolicyChatbot tracks:

Which document version answered what
When policies were updated
What responses might be outdated
Which chunks need re-embedding

Try implementing that in your weekend project.

The Performance Numbers That Matter

Let’s talk real metrics:

PolicyChatbot Performance:

Query to response: 1.8 seconds average
Accuracy (based on user feedback): 94%
Uptime: 99.9%
Concurrent users supported: Unlimited
Cost per query: £0.003

Typical DIY Performance:

Query to response: 5-10 seconds
Accuracy: 70-80% (if you’re lucky)
Uptime: “We’re working on it”
Concurrent users: Crashes at 50
Cost per query: £0.01-0.05

The difference? One is a product. The other is a science experiment.

When Building Your Own Makes Sense

Look, I’m not saying never build your own RAG pipeline. There are valid reasons:

You’re a research lab exploring new RAG techniques
You have unique requirements that no vendor supports
You’re building a RAG product to sell to others
You have unlimited budget and engineering time
You enjoy pain (no judgment)

For everyone else? Use PolicyChatbot.

The Migration Path

Already built a partially working RAG system? Not uncommon. Here’s how to migrate:

Week 1: Parallel Running

Deploy PolicyChatbot alongside your system
Upload the same documents
Compare responses
Measure performance differences

Week 2: Gradual Migration

Route 10% of queries to PolicyChatbot
Monitor feedback scores
Increase percentage as confidence grows
Document improvements

Week 3: Full Cutover

Switch primary traffic to PolicyChatbot
Keep old system as backup
Monitor for edge cases
Celebrate

Week 4: Cleanup

Shut down old infrastructure
Redeploy those engineers to actual product work
Calculate money saved
Buy the team dinner

The Hidden Benefits

What nobody tells you about using a managed RAG service:

You Get Your Weekends Back

No more:

Emergency fixes when embeddings fail
Debugging vector similarity calculations
Optimizing chunk retrieval queries
Updating prompts at midnight

You Can Focus on Your Domain

Instead of becoming a RAG expert, you can focus on:

Making your documents better
Understanding user needs
Improving business processes
Actually using the insights from analytics

You’re Always Current

When GPT-5 launches, PolicyChatbot will support it. When new embedding models emerge, they’ll integrate them. When better reranking algorithms are discovered, you’ll get them automatically.

Your DIY system? Still using techniques from 2023.

The Bottom Line

RAG is powerful technology. It’s also complex, finicky, and expensive to implement properly.

You can spend 3 months and £100,000 building a mediocre RAG pipeline that you’ll maintain forever.

Or you can spend 10 minutes and £99/month getting a production-ready system that actually works.

The math is simple. The decision should be too.

But hey, if you want to build your own, I respect that. Just remember this article when you’re debugging embedding dimensionality mismatches at 2am on a Saturday.

The rest of us will be sleeping soundly while PolicyChatbot handles our RAG pipeline.

Ready to skip the RAG pipeline headaches? Start your PolicyChatbot free trial and have a working chatbot in 10 minutes. Because life’s too short to build your own vector database.