Beyond the PoC: Engineering High-Fidelity RAG Systems with Unity Catalog

There’s a dirty secret in the GenAI world: building a demo is easy. Building a product is hard.

In an afternoon, you can ship a Proof of Concept chatbot that answers correctly 80% of the time. But in an enterprise setting—especially Finance, Healthcare, or Legal—that remaining 20% isn’t just an annoyance. It’s liability.

If a support bot hallucinates a refund policy, you lose money. If a legal bot cites a clause that doesn’t exist, you get sued.

The root cause is usually the same: most PoCs rely on pure Vector Search (semantic similarity). It’s great at concepts, but it’s weak at precision. It can confuse “Product A” with “Product B” simply because the wording is similar.

To move from a fragile demo to a high-fidelity RAG system, you can’t rely on the “magic” of the LLM. You need to engineer reliability into retrieval, ranking, prompting, and governance.

Here’s a practical blueprint using Databricks Mosaic AI tools.


1) The Retrieval Fix: Hybrid Search

Standard vector search converts text into vectors and finds the closest matches. That’s perfect for vague questions like “How do I fix my screen?” It’s much worse for identifiers like “Error Code 504”.

To a vector database, “504” and “505” can look almost identical. To an engineer, they’re completely different problems.

The solution: Hybrid Search

Hybrid search combines two signals at the same time:

  • Vector search for meaning (semantic)
  • Keyword search for exact matches (lexical)

The engine fuses the results so that when users type a SKU, error code, or policy ID, the exact match has a strong chance to win.

Technical implementation (Databricks SDK)

from databricks.vector_search.client import VectorSearchClient

vsc = VectorSearchClient()

index = vsc.get_index(endpoint_name="prod_vs", index_name="main.rag.docs_index")
# Hybrid mode combines semantic + lexical retrieval
results = index.similarity_search(
    query_text="Warranty for SKU-99X",
    query_type="HYBRID",  # <--- The critical switch
    num_results=10,
    filters="tenant_id = 'acme'"  # enforcing isolation
)

Why this matters: high-fidelity retrieval isn’t about finding the “best” answer in the entire database. It’s about finding the best answer inside the correct slice of context.


2) The Quality Uplift: Reranking

Even with strong retrieval, the right answer can still land in position #8 or #9. And if the LLM mostly pays attention to the top few chunks, you can still end up with a hallucination.

The solution: Cross-Encoder Reranking

A reranker acts like a second pair of eyes. It reads the initial candidate results and grades them against the question, then re-sorts the list so the most relevant evidence rises to the top.

Trade-off: reranking adds latency and compute cost. But for high-stakes use cases where accuracy is non-negotiable, it’s often worth it.

from databricks.vector_search.reranker import DatabricksReranker

results = index.similarity_search(
    query_text="What are the compliance risks?",
    query_type="HYBRID",
    reranker=DatabricksReranker(columns_to_rerank=["text", "title"])
)


3) Systematic Optimization: DSPy

Once retrieval is solid, the next bottleneck is usually the prompt.

Most teams do “vibes-based” prompting: tweak a sentence, try again, hope it improves. That works for prototypes, but it’s fragile in production.

The solution: DSPy

DSPy turns prompting into an optimization workflow. You define:

  • a metric (what “good” means),
  • a dataset,
  • and an optimizer that searches for better instructions and examples.

import dspy
from dspy.teleprompt import MIPROv2

def validate_answer(example, pred, trace=None):
    return dspy.evaluate.answer_exact_match(example, pred)

teleprompter = MIPROv2(metric=validate_answer)
optimized_rag = teleprompter.compile(my_rag_module, trainset=training_data)


This transforms prompt engineering from an art into a repeatable improvement loop.


4) Governance Is Quality

High-fidelity also implies security. A bot that gives the correct answer to the wrong person is still a failure—just a more dangerous one.

Unity Catalog is the control plane for governance. But it’s important to be precise: Vector Search indexes don’t behave like standard tables.

To enforce multi-tenant security (Client A never seeing Client B’s data), you need:

  • strict index permissions, and
  • mandatory metadata filtering at the application/query layer (like the tenant_id filter shown above)

If your system can’t prove it respects boundaries, it isn’t ready for production.


The Everstone Approach: Prove It

At Everstone AI, we don’t consider an AI system “production ready” until its quality can be proven with numbers.

We use MLflow Evaluation to run automated judges on every change:

  • Retrieval groundedness: did the system make it up?
  • Answer relevance: did it actually answer the question?
  • Safety: did it violate policy?

import mlflow

from mlflow.genai.scorers import RetrievalGroundedness, Safety
eval_results = mlflow.genai.evaluate(
    data=test_dataset,
    scorers=[RetrievalGroundedness(), Safety()]
)


The takeaway: if you can’t measure it, you can’t trust it. High-fidelity AI is an engineering discipline—not a magic trick.


Location: Wyoming, USA

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