AI in working with SQL – practical use of LLM models and agents for data analysis

Training process

Training needs analysis

If you have specific requirements regarding the training programme, we will carry out a training needs analysis for you. This will guide us on which aspects of the programme should receive greater emphasis, so that the training programme meets your specific needs.

What will you gain?

Smarter LLM use in analytics - You will learn when LLMs truly speed up data analysis and when they introduce unnecessary risk, so you can use AI deliberately, with better judgment and more reliable outcomes.

Faster SQL work - You will turn business questions into solid SQL faster, refactor existing queries more confidently, and catch edge cases that often distort analytical results or reporting output.

Effective analytics prompts - You will master prompts for data tasks, choose the right input format, and set clear model constraints so the responses you get are more precise, useful, and easier to validate.

Fewer wrong conclusions - You will learn how to spot hallucinations, anomalies, and inconsistent numeric outputs, making it easier to stop flawed insights before they reach a dashboard, deck, or business decision.

Stronger insight delivery - You will use AI to summarize analyses, generate product hypotheses, validate KPIs, and interpret results, so you can prepare outputs that are clearer and more useful for business partners.

Support for A/B testing - You will learn how to use AI in experiment design, hypothesis framing, and statistical interpretation without blindly accepting automated conclusions produced from test results.

AI in your daily workflow - You will see how to connect language models with databases, code, RAG, and day-to-day analyst tasks, helping you shorten ad hoc analysis, documentation, and stakeholder communication.

Safer work with data - You will learn Local-First Analytics, anonymization methods, and hybrid approaches, so you can protect sensitive data better and choose the right model setup for your organization.

Training programme

1. Introduction to AI and LLM models in the analytical context

what LLM models are and how they work, deterministic vs probabilistic AI; strengths and limitations of LLMs in data analysis,
where AI really helps the analyst, and where it harms,
the evolution of the analyst's role: from „query operator” to „systems orchestrator”,
token economics and context window management,
RLM models – selecting the model for the task,
AI systems architecture: how to connect a language model with a database and code.

2. Prompt Engineering for data analysis – fundamentals

how the LLM model „thinks” and what this means for prompts, the structure of an analytical prompt,
context engineering, data preparation (Markdown, JSON, XML, CSV),
system prompt – defining roles, constraints and output formats,
generation and refactoring of SQL queries,
translating business requirements into analytical queries,
analysis of existing queries (readability, optimization, edge cases),
„memory” of AI systems – RAG, Agentic RAG, vector databases and graph databases.

3. Analytical case studies – working with data and insights

automatic summaries of analysis results, generation of product insights and hypotheses, sanity checks: detection of anomalies, gaps, illogical results,
creation and validation of KPIs,
interpretation of A/B test results with the help of AI, identification of potential errors in data and analyses.

4. A/B tests and experiments using AI

AI support in designing experiments,
formulating test hypotheses, interpretation of statistical results,
supporting inference and communication of results,
risks of automatic interpretations of A/B tests,
„synthetic users” (AI Personas).

5. Workshops: real team scenarios

work on real examples from the team,
optimization of existing analytical prompts,
testing different variants of prompts,
comparing results and quality of responses,
assessment of the usefulness of AI in specific use cases.

6. Integration of AI with the analyst's daily work

AI as the analyst's „copilot”, not a replacement,
use of AI in:
- ad-hoc analyses,
- data exploration,
- analysis documentation,
- communication of insights to the business,
automation of repetitive analytical tasks,
best practices of teamwork with AI.

7. Quality, ethics and safety of working with AI

validation of results generated by AI, analyst's responsibility for the results, how not to "let through" an incorrect insight,
methods of detecting hallucinations in numbers and facts,
auditability and "Explainable AI", "LLM-as-a-Judge",
running open-source models (Llama 3, Mistral, Gemma) using (Ollama, LM Studio),
analysis of sensitive data without sending it to the cloud (Local-First Analytics),
techniques of anonymizing and masking data before sending it to external APIs. Hybrid approach,
good organizational practices.

8. Summary and recommendations for the team

when to use AI, and when to use classical methods,
checklists for analytical work with AI,
recommended workflow of an analyst supported by AI,
areas for the team's further development.

What are the prerequisites for participating in the training?

SQL basics - You should be comfortable reading and writing simple SQL queries, including SELECT, JOIN, WHERE, GROUP BY, and aggregations, to fully benefit from the hands-on exercises.

Analytics experience - You should have practical experience in data analysis or reporting so you can follow business questions, KPIs, and common data quality issues discussed during the training.

Working with data - You should understand tables, columns, filters, and common data formats such as CSV or JSON, so you can work smoothly with the examples and prompts used in the course.

Basic test statistics - You should know core concepts used in experiment analysis, such as hypothesis, metric, and significance, so you can properly follow the section devoted to A/B testing.