Most AI systems that interact with data try to go straight from natural language to SQL. That works in isolated demos, but it falls short in real organizations.

The reason is simple: the model lacks context.

It doesn’t know how your team defines a metric, which join paths are valid, what tables are sensitive, or what logic is commonly reused. These are all things hidden in past queries, not in your schema.

That is where RAG, or retrieval-augmented generation, comes in.

What Is RAG and Why Does It Matter for SQL?

RAG is a technique that augments an LLM's prompt with relevant context retrieved from external sources.

Instead of relying only on the model’s training, it first retrieves examples, metadata, or prior queries to condition the generation.

In the context of SQL, this means:

• Pulling similar queries to use as few-shot examples

• Fetching schema information, table usage, or joins from past work

• Injecting user or org-specific definitions into the prompt

• Using real-world patterns to prevent hallucination or misuse

Without RAG, even the best fine-tuned model will eventually guess.

The Retrieval Layer Is the Hard Part

Most companies experimenting with text-to-SQL don’t struggle with generation.

The hard part is retrieval:

• What are you retrieving from?

• How do you structure the data for fast and relevant lookup?

• How do you map user intent to the right past queries or definitions?

  
  

This requires:

• Indexing SQL in context (not just the raw string)

• Embedding queries based on structure, tables used, and semantics

• Normalizing different query styles across sources like dbt, Snowflake, or notebooks

• Ranking results based on intent match, not just lexical similarity

Sherloq is designed to handle all of that.

RAG for SQL Workflows

Sherloq integrates directly into where SQL lives.

It pulls queries from Snowflake worksheets, dbt models, GitHub, BI tools, and more. Then it processes them into a structured, searchable form.

Here’s what Sherloq does under the hood:

• Extracts queries with metadata like authorship, runtime, and table usage

• Parses and normalizes the query structure to understand logic patterns

• Embeds queries using a combination of syntax trees, table lineage, and vector models

• Indexes queries for fast retrieval based on natural language input, table names, or semantic similarity

Final Thought

Language models are powerful, but they need help. Especially in enterprise data, where context is everything.

Sherloq gives AI the context it needs to work reliably in real-world analytics workflows. We don’t just generate SQL. We retrieve the knowledge behind it.

That is how you go from generative AI to useful AI in analytics.