AI becomes valuable when it works in the real world.

Distinguish language models, AI services, copilots and specialised tools by task, data access and degree of integration. Criteria such as output quality, data protection, cost, currency and integration into existing workplaces make selection transparent.

Learn which tasks generative AI can support reliably and where probabilistic output, hallucinations, bias, context limits or outdated knowledge create risk. Common failure modes are recognised, assessed and connected to appropriate checks.

Translate unclear work requests into structured prompts containing context, task, inputs, objective, quality criteria and output format. This makes requirements transparent, results easier to compare and follow-up questions more focused.

Improve prompts in controlled steps, compare variants and distinguish professional weaknesses from wording or context problems. Proven patterns are versioned, documented and made reusable for similar tasks.

Review AI output for professional accuracy, completeness, contradictions, source grounding and fitness for purpose. Checklists, challenge questions and human approval points help correct weak results deliberately.

Work on common tasks such as drafting, summarising, comparing, extracting, classifying and preliminary research. Clarify required inputs, result validation and where AI usefully supports the existing workflow.

Classify personal, confidential and business-critical information and decide what data an AI service may process. Anonymisation, permissions, provider settings, internal rules and safer alternatives are assessed through practical situations.

Apply the methods to transparent tasks, compare alternative approaches and reflect on the impact of your decisions. Exercises combine explanation, hands-on work, feedback and a transferable improvement.

Receive and use checklists, structures, examples and concise references that remain useful after the seminar. Materials are not merely distributed; they are explained and related to common application situations.

Map a real workflow with triggers, inputs, roles, decisions, handovers and outputs. Assess where AI can help, which controls are required and how a reliable pilot fits into the process chain.

Discover existing text, documents and knowledge through source structure, metadata, terminology and suitable search or retrieval approaches. Quality, currency, permissions and the limits of a RAG or knowledge solution are made explicit.

Place data objects, databases, interfaces, ETL pipelines and enterprise software into one coherent solution view. System boundaries, data flows, responsibilities, error paths and operational requirements are considered.

Define measurable expectations, validation rules and approval points for inputs, processing and outputs. Sampling, logging, escalation and human accountability create controlled use instead of blind trust.

Work on realistic cases from requirements engineering, business analysis, project management, development, architecture, quality and testing. Roles, system dependencies, trade-offs and technical constraints are explicitly included.

Develop prompt structures, analysis templates and workflow patterns that can be reused with clear inputs, checks and ownership. Versioning and adaptation prevent templates from becoming uncontrolled one-off solutions.

Create a tangible result for your own work, such as a prompt template, analysis, checklist, process sketch or pilot description. Next steps, ownership and success criteria make the transfer actionable.

Before an in-house session, audience, knowledge level, systems, assignment and desired outcomes are aligned. Examples, depth, exercises and transfer tasks are adapted to the actual organisational context.

For regulated, safety-critical or highly specialised content, the roles and responsibilities of a dual-instructor team are defined. CTPM covers AI, IT, data, architecture and methodology; the sector expert adds processes, terminology and binding requirements.

Structure stakeholders, objectives, system boundaries and requirements using suitable elicitation, modelling and documentation techniques. Quality criteria, acceptance conditions, reviews and traceability connect business and delivery.

Derive architecture goals from business and quality requirements and structure components, interfaces and responsibilities. Architecture decisions, risks, modernisation steps and transparent documentation are assessed together.

Transform business concepts and rules into conceptual, logical and relational data models. Keys, relationships, normalisation, integrity, SQL queries and performance are treated as connected design decisions.

Design the path from source to target systems with mappings, transformations, loading approaches and quality rules. Delta logic, error handling, restart, monitoring, metadata and lineage are considered from the start.

Capture functions, data, interfaces, dependencies, documentation and implicit knowledge in an established solution. Risks and unknown areas become visible so modernisation can proceed incrementally without uncontrolled changes to current behaviour.

Practical module with explanation, exercise, reflection and a reusable transfer result.

Practical module with explanation, exercise, reflection and a reusable transfer result.

Practical module with explanation, exercise, reflection and a reusable transfer result.

Practical module with explanation, exercise, reflection and a reusable transfer result.

Practical module with explanation, exercise, reflection and a reusable transfer result.

Practical module with explanation, exercise, reflection and a reusable transfer result.

Practical module with explanation, exercise, reflection and a reusable transfer result.

Practical module with explanation, exercise, reflection and a reusable transfer result.

Work with Oracle-specific SQL and PL/SQL from data access and transactions to procedures, functions and packages. Exception handling, performance, testing, interfaces and maintainability are connected through realistic database tasks.

Practical module with explanation, exercise, reflection and a reusable transfer result.

Practical module with explanation, exercise, reflection and a reusable transfer result.

Practical module with explanation, exercise, reflection and a reusable transfer result.

Practical module with explanation, exercise, reflection and a reusable transfer result.

Practical module with explanation, exercise, reflection and a reusable transfer result.

Practical module with explanation, exercise, reflection and a reusable transfer result.

Practical module with explanation, exercise, reflection and a reusable transfer result.

Practical module with explanation, exercise, reflection and a reusable transfer result.

Source, requirement, design, change, test, approval and release are linked so decisions and effects remain explainable.

The method is practised with SQL, PL/SQL, MySQL/MariaDB, PHP, JSON, HTML, CSS, JavaScript, SQL*Plus and Bash without turning the seminar into ten isolated language courses.

Normal cases, edge cases, side effects and unacceptable outcomes are translated into verifiable tests, regression checks and approval criteria.

Release preparation, configuration, scripts, handover, monitoring, fallback and maintenance effects are treated as one operational process.

Observations, logs, reproduction steps, hypotheses and counterchecks are separated so plausible AI explanations are not mistaken for verified findings.

Decisions, assumptions, interfaces, operating notes, open risks and validation evidence are documented clearly and maintainably.

Affected components, data, interfaces, existing logic and possible side effects are identified and included in validation before change.

AI provides working drafts. People define goals and boundaries, review evidence and tests, decide on correction and approve further use.

An anonymised case shows how requirements, database, PHP, web technology, testing, installation and documentation are iteratively supported by AI and professionally approved.

Oracle SQL and PL/SQL context, data models, transactions, packages, failure modes, test data, reporting and documentation are treated together rather than in isolation.