Orchestrate complementary tools
How can you connect several tools to a generative AI without losing traceability or control over the result?
3500 € per session, excl. VAT
In short
Over two days, totalling 14 hours excluding breaks, you build a prototype linking data collection, AI-assisted processing and presentation of results. You define exchanges between tools, human validations and error recovery, then measure quality using a test set before preparing a limited pilot.
Why this training?
Using assistants and files side by side increases repeated data entry, incompatible formats and silent errors. An explicit architecture makes it clear which step produces each piece of information and who authorises its use. Generative AI can extract information and draft text, but it replaces neither a reference check nor authorisation to send. The training separates generation, deterministic checks and human decisions to make the workflow testable. Running case study: The fictitious organisation Avelorne Équipements receives requests for parts via a form. Using a catalogue and fictitious requests, the team must extract the references, check that they exist, prepare a response and record a validation before anything is sent. Transfer to the workplace: Adapt the prototype to an authorised process, have access permissions approved, then launch a restricted pilot with a designated owner, a review of errors and a documented decision to continue or stop.
Objectives of this training
- Map the steps, dependencies and validations of a multi-tool workflow
- Specify exchange formats and rules
- Assemble a prototype using authorised tools
- Test error scenarios and measure the quality achieved
- Define the scope and conditions of a pilot
Who is it for and what are the prerequisites?
Roles and tasks concerned
- Process owners and AI project managers
- Key contacts for business operations, automation or continuous improvement
Prior skills
- Use generative AI and structure your instructions.
- Know a business process and work with tabular data.
- Understand the principles of importing, exporting and access permissions.
What you will work on
Define the boundaries of the workflow
Objective: Establish a testable boundary. Trigger, expected outcome, exclusions and responsibilities.
Allocate functions
Objective: Assign each task to the right component. Collection, generation, retrieval, checking and storage.
Formalise exchange contracts
Objective: Avoid ambiguities between steps. Mandatory fields, types, identifiers and missing values.
Set boundaries for generation
Objective: Limit unusable outputs. Instructions, sources, expected structure and abstention.
Organise safeguards
Objective: Check data and decisions. Minimum access permissions, retention, malicious instructions and validation.
Assemble an initial workflow
Objective: Run a complete case. Documented manual transfers or available connections.
Prepare the tests
Objective: Make the evaluation reproducible. Standard cases, duplicates, unknown references and expectations.
Plan for interruptions
Objective: Resume without duplicating. States, logging, retry and stopping.
Measure the results
Objective: Quantify deviations. Extraction accuracy, completeness, interventions and observed duration.
Correct and retest
Objective: Verify the effect of the changes. Step-by-step diagnosis and regression testing.
Define the scope of the pilot
Objective: Define a controlled experiment. Scope, supervision, thresholds and fallback to manual processing.
Obtain acceptance of the prototype
Objective: Enable a third party to take over. Documentation, known limitations and a reasoned decision.
What you take away
- Process mapping and exchange contracts
- Documented prototype and version-controlled instructions
- Test set and quality report
- Pilot plan with a recovery procedure
Method and assessment
- Short teaching inputs followed by immediate practice on the case study used throughout.
- Individual work followed by work in pairs, with peer review of the outputs.
- Guided group discussion: what works, what needs correcting and what remains a human decision.
- Preparation: Identify the authorised tools and verify their actual data exchange capabilities. Prepare an anonymised case or use the fictitious case file provided.
Observable criteria: Each transition specifies the format, check and responsible person. ; The proposed references are verified against the catalogue. ; An incorrect input triggers explicit handling. ; No transmission bypasses human validation. ; The report compares expected and actual results against defined thresholds..
Frequently asked questions
Is programming required?
No; exchanges may remain manual, but must be specified and reproducible.
Which tools should you use?
Only those authorised by the organisation, according to their verified data exchange capabilities.
Can you use real data?
The exercises use only fictitious or anonymised cases.
Will the workflow be made production-ready?
No: the expected outcome is a tested prototype and a pilot plan.
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