Answers supported by documentary evidence
How can you obtain traceable answers from a controlled document corpus?
3500 € per session, excl. VAT
In short
Over two days, comprising 14 hours excluding breaks, you design a prototype for document search enhanced by generative AI. You prepare a corpus, configure the search, ground answers in sources and measure their quality. You leave with a test protocol and a pilot plan, without aiming for production-scale deployment.
Why this training?
A keyword search sometimes retrieves an outdated manual rather than the applicable procedure. Without metadata, priority rules and verifiable citations, a fluent answer can conceal a documentation error. Retrieval-augmented generation, or RAG, links answers to retrieved passages. Its reliability depends on the corpus, chunking and checks; it is measured using representative questions, not just a convincing demonstration. Running case study: Aveltor Équipements, a fictitious organisation, has contradictory maintenance manuals and procedures. Its technical support team must find the applicable instruction for each model, cite its version and refuse to answer when documentation is missing. Transfer to the workplace: Launch a limited pilot within a validated documentary scope, in accordance with internal permissions; compare its results against the reference set before any decision to expand.
Objectives of this training
- Defining a corpus and its access rules
- Structuring documents and their metadata
- Configuring a search prototype with source-backed answers
- Measuring document relevance and answer faithfulness
- Formalising the conditions for launching a pilot
Who is it for and what are the prerequisites?
Roles and tasks concerned
- Documentation and knowledge management leads
- AI leads, digital project managers and technical support managers
Prior skills
- Use generative AI and verify its answers.
- Know the documentation processes in your professional field.
- Be able to work with structured files and understand technical configuration.
What you will work on
Defining the scope of use
Objective: Define the scope of the prototype. Target questions, users, exclusions and RAG architecture.
Assessing the corpus
Objective: Identify usable sources. Versions, duplicates, extraction quality and confidentiality.
Preparing passages
Objective: Preserve documentary context. Chunking, overlap, tables and metadata.
Configuring search
Objective: Retrieve the applicable passages. Lexical or semantic search depending on the environment; version filters.
Setting boundaries for generation
Objective: Produce justified answers. Citations, contradictions and abstention.
Building the reference framework
Objective: Define a reproducible evaluation. Expected answers, supporting passages and difficult cases.
Measuring errors
Objective: Distinguish retrieval from generation. Presence of evidence, accuracy, citations and appropriate abstentions.
Correcting without overfitting
Objective: Verify the effect of changes. Failure diagnosis; parameters and held-out tests.
Testing safeguards
Objective: Identify document exposure risks. Access checks before retrieval, malicious instructions in documents, logging.
Preparing the pilot
Objective: Organise a controlled experiment. Updates, source removal, oversight and stopping criteria.
What you take away
- Prepared corpus with a source register
- Searchable prototype and configuration sheet
- Reference set and evaluation report
- Pilot plan and maintenance procedure
Method and assessment
- Short instructional inputs followed by immediate practice using the case study that runs throughout the course.
- Individual work followed by work in pairs, with peer review of the outputs.
- Facilitated group discussion: what works, what needs correcting and what remains a human decision.
- Preparation: Obtain approval for an authorised environment that supports ingestion, retrieval and generation; verify its capabilities beforehand. Prepare fictitious or anonymised documents and business-related questions, without confidential information.
Observable criteria: Each document has a source, a version and an access status. ; Citations refer to the passages actually used. ; Questions with insufficient evidence trigger an explicit abstention from answering. ; Measurements distinguish between passage relevance and answer faithfulness. ; The pilot specifies responsibilities, acceptance thresholds and stopping conditions..
Frequently asked questions
Is programming required?
Not necessarily; the learning pathway depends on the verified capabilities of the authorised environment.
Can we use our own documents?
Only fictitious or anonymised documents approved for use with the selected tools.
Will the answers always be accurate?
No; citations, abstentions from answering and tests make the limitations observable.
Will the prototype be production-ready?
No; security, operations and integrations require additional validation.
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