Building Production RAG Pipelines: 3 Lessons from My First Year

How I went from RAG beginner to production-ready, handling 50K+ queries per month

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TL;DR

I spent a year building RAG (Retrieval-Augmented Generation) systems at TCS, going from experiments to production systems handling 50,000–80,000 HR queries monthly. Here are 3 hard-earned lessons about chunking, embedding models, and retrieval strategies that I wish I knew on day one.

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The Problem: HR Was Drowning in Queries

Picture this: HR teams fielding 50,000 to 80,000 queries every month. Most were repetitive: "How do I apply for leave?" "What's the laptop policy?" "When is the next pay cycle?"

The knowledge existed in documents, but finding it was painfully slow.

Our team decided to build a RAG-powered system that could:

1. Understand natural language questions
2. Retrieve relevant policy documents
3. Generate accurate, contextual answers

Here's what I learned.

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Lesson 1: Chunking Strategy Makes or Breaks Your RAG

The Mistake

My first attempt? Split documents every 500 characters with 50-character overlap. Simple, right?

Result: Terrible retrieval. We were cutting sentences mid-way and losing context.

The Fix

I learned to respect semantic boundaries:

• Preserve sentences: Don't split mid-sentence unless absolutely necessary
• Paragraph awareness: Keep related sentences together
• Context windows: Ensure each chunk has enough context to be understood independently

# What I implemented
def smart_chunk(text, chunk_size=512, overlap=50):
    """Chunk text while preserving sentence boundaries"""
    chunks = []
    start = 0

    while start < len(text):
        end = min(start + chunk_size, len(text))

        # Look for sentence boundary in overlap region
        if end < len(text):
            search_start = max(end - 100, start)
            sentence_end = text.rfind('. ', search_start, end)
            if sentence_end != -1:
                end = sentence_end + 1

        chunks.append(text[start:end].strip())
        start = end - overlap

    return chunks

The Impact

40% reduction in manual HR queries after implementing semantic chunking.

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Lesson 2: Not All Embedding Models Are Equal

The Mistake

I started with the first embedding model I found. It seemed to work in testing.

Result: In production, similar queries were retrieving completely wrong documents. "Leave policy" was returning "Laptop policy" because both contained words like "application" and "request."

The Fix

I learned to evaluate embeddings for my specific domain:

Model	Size	Speed	Domain Fit
all-MiniLM-L6-v2	80MB	Fast	General purpose
all-mpnet-base-v2	400MB	Medium	Better semantic understanding
domain-specific	Varies	Varies	Requires fine-tuning

What worked for us:

• Start with all-MiniLM-L6-v2 for prototyping
• Upgrade to all-mpnet-base-v2 for production
• Consider domain fine-tuning if you have 10K+ documents

Key Insight

Test your embeddings with real queries from actual users. Synthetic test data lies.

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Lesson 3: Hybrid Search Beats Pure Semantic Search

The Mistake

I relied solely on vector similarity search. It worked great for conceptual queries like "What's our remote work policy?"

Result: Failed miserably on specific queries like "Leave policy version 3.2" or acronyms like "PTO".

The Fix

I implemented hybrid search: combine semantic search with keyword matching.

# Simplified hybrid search approach
def hybrid_search(query, top_k=5):
    # Semantic search
    semantic_results = vector_store.similarity_search(query, k=top_k)

    # Keyword search (BM25 or simple TF-IDF)
    keyword_results = keyword_search(query, k=top_k)

    # Combine and re-rank
    combined = merge_results(semantic_results, keyword_results)
    return combined[:top_k]

The Results

• Conceptual queries → Semantic search wins
• Specific queries → Keyword search wins
• Hybrid → Best of both worlds

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Bonus: The Forgotten Piece - Evaluation

I almost forgot to mention: how do you know if your RAG is actually good?

I learned to track:

• Retrieval accuracy: Did we get the right documents?
• Answer relevance: Does the generated answer actually help?
• User feedback: Simple 👍/👎 buttons teach you more than metrics

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Where I Am Now

That first RAG system? It's now handling queries across 3 different business workflows at TCS. The HR team? They went from drowning to actually having time for strategic work.

I'm now working on LangGraph-powered agentic workflows and still learning every day.

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Key Takeaways

1. Chunk smart, not just small - Respect semantic boundaries
2. Test embeddings with real data - Your domain matters
3. Hybrid search > pure semantic - Combine approaches
4. Measure everything - You can't improve what you don't track

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Your Turn

Building RAG systems? I'd love to hear about your challenges.

What's your biggest pain point with RAG right now?

• Chunking strategies?
• Embedding model selection?
• Retrieval accuracy?
• Something else?

Drop a comment—let's learn together!

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About Me

I'm Mahek, an AI Engineer at TCS with a passion for making complex AI accessible. When I'm not building RAG pipelines, I'm conducting workshops (upsilled 60+ engineers so far!) or writing about lessons from production systems.

🔗 LinkedIn | 💻 GitHub

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