Design schema linking and context so natural language questions map to the right tables.
Digital Transformation & Artificial Intelligence
Text-to-SQL and Natural Language Querying of Enterprise Databases
Build text-to-SQL systems that turn natural language into safe, accurate queries against enterprise databases, with governance controls designed in from the start.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
A business question about enterprise data often dies in a queue waiting for an analyst to write the SQL, yet pointing a language model directly at a production database without safeguards simply trades one problem for a more dangerous one. This course builds text-to-SQL systems that turn natural language into queries that are both accurate and safe: schema linking and context design so a model maps ordinary business terms to the right tables, execution feedback loops that let a generated query correct itself after a database error, and a maintained glossary that resolves ambiguous terms and relative time periods the same way every time. Multi-table joins, mixed-grain aggregation and conversational follow-up questions are handled directly, before the modules that matter most in production: read-only least-privilege execution, row-level security, query cost limits and full audit logging of every question asked. Accuracy is measured by comparing executed results rather than matching query text, and the closing module deploys the whole capability inside the business intelligence tools people already open every day.
Expected Learning Outcomes
Build execution feedback loops that let a generated query self-correct after a database error.
Resolve ambiguous business terms and relative time expressions through a maintained glossary.
Generate correct multi-table joins and aggregations for realistic analytical questions.
Restrict generated queries to read-only, least-privilege roles with row-level security enforced.
Evaluate query accuracy by comparing executed results rather than exact query text.
Deploy natural language querying inside existing business intelligence tools at scale.
Who Should Attend
Data engineers and analytics teams building self-service natural language query tools.
Business intelligence leaders reducing the backlog of ad hoc reporting requests.
Data governance staff responsible for access control and query safety on enterprise databases.
Product managers embedding natural language querying into internal or customer-facing tools.
Database administrators evaluating the security implications of AI-generated queries.
Business analysts who need direct answers from enterprise data without writing SQL.
Course Modules
Select any module to see its sessions and points.
01Foundations of Text-to-SQL Systems
2 sessions · 8 points
Session 1Schema Linking and Context Design
- Mapping natural language terms to the correct tables and columns through schema linking.
- Curating column descriptions and business glossaries that give the model context a bare schema lacks.
- Retrieving only the relevant schema subset for large databases instead of passing every table into a prompt.
- Providing few-shot query examples that reflect the organisation's own naming conventions.
Session 2From Prompt to Executable Query
- Generating an intermediate representation before final SQL to reduce syntax errors on complex joins.
- Executing generated SQL against a sandboxed connection to catch errors before they reach a user.
- Applying self-correction loops that feed a database error back to the model for an automatic retry.
- Formatting returned result sets into a table, chart or plain-language answer depending on the question.
02Handling Ambiguity and Complex Business Questions
2 sessions · 8 points
Session 1Disambiguating Intent and Business Terms
- Asking a clarifying question when a query could reasonably map to more than one interpretation.
- Resolving synonyms and abbreviations against a maintained, centrally owned business glossary.
- Interpreting relative time expressions such as last quarter against the organisation's fiscal calendar.
- Confirming which of several similarly named metrics a question is actually asking for.
Session 2Multi-Table Joins, Aggregation and Follow-Up Questions
- Generating correct joins across many related tables without duplicating or dropping rows.
- Applying the right aggregation level and grouping for questions that mix detail and summary requests.
- Maintaining context across a conversational follow-up question that refines an earlier query.
- Detecting when a follow-up question requires a fresh query rather than a filter on the previous result.
03Governance, Security and Query Safety
2 sessions · 8 points
Session 1Access Control and Safe Execution
- Restricting generated queries to a read-only, least-privilege database role regardless of model output.
- Ensuring row-level security and data masking apply identically to natural language and existing reports.
- Setting query cost and complexity limits to block accidental full-table scans or runaway joins.
- Blocking prompt injection attempts embedded in stored data the model might retrieve and misread as instructions.
Session 2Auditing and Standardising Business Definitions
- Logging every natural language question alongside its generated SQL and result for audit and troubleshooting.
- Maintaining a semantic or metrics layer so a term like revenue resolves consistently across tools and teams.
- Reviewing a sample of generated queries regularly to catch drifting accuracy on new question types.
- Escalating queries the system cannot answer confidently to a data analyst rather than guessing.
04Evaluating Accuracy and Scaling Adoption
2 sessions · 8 points
Session 1Measuring Query Accuracy
- Building an evaluation set of real business questions with verified correct SQL and results.
- Measuring execution accuracy by comparing returned results rather than matching exact query text.
- Tracking accuracy separately for simple lookups versus multi-table analytical questions.
- Identifying recurring failure patterns to prioritise schema documentation or glossary fixes.
Session 2Deploying Across the Organisation
- Embedding natural language querying inside business intelligence tools users already work in.
- Caching frequently asked questions to reduce cost and response time on common queries.
- Training business users on how to phrase questions and interpret the system's confidence signals.
- Monitoring adoption and satisfaction to identify teams needing additional glossary or schema investment.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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