AI trainingDecision guide
An open-web research workflow helps discover candidate sources. A bounded document workflow helps analyze an approved pack. Either can support a brief, but neither guarantees complete evidence or reliable citations. Decide whether the main gap is finding material or extracting and comparing material you already trust, then build a source-checking step into the work.
Look for query design, search strategy, and writing from sources. Open original publications, confirm who produced the evidence, and record dates and methods. Search results and generated summaries help locate material but do not prove the source is relevant or support a claim.
Notebook-style training can help query an approved set of documents and produce learning or briefing outputs. Decide which versions are in scope, record owners and dates, and preserve a gap list. If the needed evidence is missing, the bounded pack cannot answer by itself.
Related verification instruction follows source preparation through fact-checking and editing. For each important brief claim, record the source passage, version, date, unit, and scope. Have a responsible person correct, qualify, remove, or hold unsupported wording before circulation.
Fictional nonprofit example
Fictional scenario: a program officer needs a two-page brief on youth employment. The researcher discovers candidate reports, checks original publications, then compares them with an approved internal pack. A claim-to-source table flags unsupported numbers and conflicting dates before drafting.
Use open-web training to find candidate evidence and approved-document training to analyze a controlled pack. Verify original sources and each outgoing claim before sharing.
Provider A–Z. Compare the particular curriculum and practice description; no quality rank is implied.
Choose a research topic, build a blueprint with search terms, questions, hypotheses and plan, compare claims and metric notes for patterns, contradictions and gaps, then write an audience-specific brief and executive summary.
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Learners define a monitoring question, organise RSS/newsletter/social sources and compare their reliability and currency. They prompt article summaries, verify sources and bias and build a monitoring assistant. A progressive no-code case then connects collection, triage, distribution and archiving in a simple automation. A scenario-based online questionnaire evaluates the advertised learning.
EUR 990 excluding tax seat · EUR 2690 excluding tax group rate for up to 12 participants, trainer travel/lodging extra
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Participants create a grounded notebook, prepare and import a source corpus, add research, then compare and question documents. They check accuracy, completeness and coherence before producing a synthesis, podcast and distribution support. The continuing case supplies a restitution kit.
2027: EUR 1030 excluding tax per seat · EUR 2850 excluding tax group up to 12 · travel excluded
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The sequence examines cognitive/emotional bias and algorithmic amplification, then recognises manipulated material including hallucinations and deepfakes. A five-step verification method checks author/date/intention, cross-checks sources, analyses content, inspects visuals and restores context. Individual cases, peer/trainer feedback and an online questionnaire support application before sharing.
EUR 470 per seat excluding tax · Group rate: 2590 for up to 12 participants · group currency and tax treatment not established
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Create a notebook, add and organize approved sources, evaluate source quality and ask source-grounded questions. Customize reports and study guides, generate source-based flashcards and quizzes with chosen difficulty, and assemble maps, audio and slide outputs.
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Ask, refine and deepen Perplexity research questions, inspect citation credibility and cross-check conflicting claims, then build an evidence-based article outline. Broader lessons apply writing, interviews, travel and photography workflows.
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LAMZ demonstrates research tools for finding, organizing, checking and presenting academic literature. The project builds a ResearchRabbit collection, tags and annotates papers, then submits screenshots and a workflow explanation for community feedback. Its reference to lesson two conflicts with the visible ResearchRabbit demonstration position.
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Louise Laurie demonstrates a six-part brief, reviews an audience report, sets a recurring research workflow and turns approved findings into a content plan. The project selects one recurring task, writes and tests a brief, and shares a reflection. Participants lacking Work mode can prepare the brief without running automation.
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Louise Laurie develops a SWOT, reviews market and competitor evidence, constructs a persona, selects measurable goals and tactics, then chooses indicators. Learners submit a one-page strategy or selected section. This one-off strategy output differs from the creator’s recurring report workflow.
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Test NotebookLM interface, prepare and organize PDF/document/link sources into a notebook, refine targeted queries and inspect responses, then produce an activity-specific synthesis with critical reliability checks.
EUR 850HTseat
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