Model Online / On-Site
Duration
Modules 6 modules

Course Objective & Scope

Waiting on data teams for every ad-hoc breakdown slows business decisions, yet dumping a hundred-table enterprise schema into an LLM prompt leads to broken JOINs, hallucinated columns and wrong numbers. This course covers end-to-end Text-to-SQL engineering: schema linking, column value lookup, dynamic few-shot SQL retrieval, SQL dialect handling and execution-guided self-correction.

Participants build a multi-turn conversational analytics assistant over an enterprise data warehouse that generates SQL, executes it safely, and returns tables, chart specs and concise narrative explanations.

Who Should Attend?

  • Data and AI engineers building natural-language interfaces over data warehouses and BI platforms
  • Analytics engineers and data scientists creating self-service reporting assistants
  • Backend developers adding embedded conversational analytics to enterprise products
  • Prerequisite: strong SQL skills and experience using LLM APIs in Python

Key Takeaways

  • Build schema-linking pipelines that select only the relevant tables and columns from large databases
  • Enrich DDL metadata with column descriptions, sample values and categorical value lookups
  • Implement dynamic few-shot retrieval over curated (question, verified SQL) pairs
  • Handle dialect-specific date arithmetic, window functions and aggregations across PostgreSQL, BigQuery, Snowflake or T-SQL
  • Support multi-turn analytical follow-ups, ambiguity resolution and automated chart specification

Curriculum

Text-to-SQL Architecture and Why Raw DDL Prompts Fail
  • Enterprise schema challenges: cryptic column names, hundreds of tables, implicit JOIN paths and coded values
  • End-to-end flow: intent -> schema/value linking -> few-shot retrieval -> SQL generation -> validation -> presentation
Schema Linking and Cell-Value Grounding
  • Selecting the relevant sub-schema (tables, columns and foreign-key paths) for a query
  • Matching user terms ("Marmara region", "active tier") to exact database literals (`region_id=2`, `status='A'`)
Dynamic Few-Shot SQL Retrieval and Dialect Engineering
  • Retrieving the closest verified SQL patterns from a curated golden query library
  • Handling CTEs, window functions and dialect-specific date/time syntax
Execution-Guided Self-Correction and Ambiguity Handling
  • Bounded repair loops for syntax errors, runtime exceptions and suspicious empty results
  • Stating assumptions explicitly or prompting for clarification when timeframes or dimensions are ambiguous
Conversational Follow-Ups, Chart Generation and Narrative Synthesis
  • Stateful follow-ups: "now break that down by channel" and "compare with last quarter"
  • Generating chart specifications (Vega-Lite / Plotly) and grounded natural-language summaries
Hands-On: Conversational Analytics Assistant on an Enterprise Schema
  • Building an end-to-end Text-to-SQL service over a multi-table analytical database
  • Testing accuracy and latency on realistic business questions

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