Modes of engagement<\/strong><\/h2>\n\n\n\nLabs support different modes and depths of engagement:<\/p>\n\n\n\n
Acclimatisation<\/strong><\/p>\n\n\n\n\n- For newcomers to AI<\/li>\n\n\n\n
- Gentle exposure to prompts, outputs, and concepts<\/li>\n\n\n\n
- Encourages questioning and scepticism before application<\/li>\n\n\n\n
- May involve guided demonstrations, tool walkthroughs, or annotated examples<\/li>\n<\/ul>\n\n\n\n
Experimentation<\/strong><\/p>\n\n\n\n\n- Structured or open-ended trials using realistic tasks<\/li>\n\n\n\n
- Focus on how to prompt, what works, what fails<\/li>\n\n\n\n
- May include prompt tuning, error analysis, or process re-runs<\/li>\n\n\n\n
- Experiment with domain-specific use cases and workflows<\/li>\n\n\n\n
- Develop concepts for applying AI in domain-specific processes<\/li>\n<\/ul>\n\n\n\n
Proof of Concept<\/strong><\/p>\n\n\n\n\n- Goal-driven tests for specific problems or use cases<\/li>\n\n\n\n
- Seeks to answer: \u201cCould this actually work for me?\u201d<\/li>\n<\/ul>\n\n\n\n
Iteration<\/strong><\/p>\n\n\n\n\n- Reworking previous attempts using feedback, insights, or new tools<\/li>\n\n\n\n
- Begins to build reusability and generalisation<\/li>\n<\/ul>\n\n\n\n
\n- <\/li>\n<\/ol>\n\n\n\n
\n\n\n\nParticipant roles<\/strong><\/h2>\n\n\n\nWe use \u201cparticipant\u201d as a general term for anyone involved in a lab. Within that, three main roles are distinguished:<\/p>\n\n\n\n
\n- Observer<\/strong> \u2013 Watches AI in use, reflects, and asks questions without interacting directly<\/li>\n\n\n\n
- Explorer<\/strong> \u2013 Actively tries prompts, evaluates responses, and engages with the tools<\/li>\n\n\n\n
- Facilitator<\/strong> \u2013 Sets up and runs the lab, provides guidance, and curates outputs<\/li>\n<\/ul>\n\n\n\n
\n\n\n\nLab setup and structure<\/strong><\/h2>\n\n\n\nEach lab setup includes:<\/p>\n\n\n\n
\n- Name and Purpose<\/strong> \u2013 A short title and clear reason for the lab<\/li>\n\n\n\n
- Mode of Engagement<\/strong> \u2013 Whether the session is for acclimatisation, experimentation, proof of concept, or iteration<\/li>\n\n\n\n
- Simulated Context<\/strong> \u2013 What kind of real-world work is being modelled<\/li>\n\n\n\n
- Participants and Roles<\/strong> \u2013 Who\u2019s involved and how<\/li>\n\n\n\n
- Task Set<\/strong> \u2013 The specific activities or challenges being explored<\/li>\n\n\n\n
- Tool Configuration<\/strong> \u2013 Which AI and non-AI tools are used, and how they are set up<\/li>\n\n\n\n
- Resources<\/strong> \u2013 A file library, sample documents, and any relevant data inputs<\/li>\n\n\n\n
- Lab Outputs<\/strong> \u2013 Deciding what types of output matter for this lab, and providing the facilities to produce them (eg templates, folder structure) <\/li>\n<\/ul>\n\n\n\n
Our position is that people take part in a lab session to explore, alongside fellow explorers, what they can do with their own AI tools. They can begin with a free licence, if that’s all they have. Then, as we and others demonstrate the greater capability of the paid licences, free uses might choose to upgrade themselves in order to continue their AI learning curve. The point here is that we are not delivering a training course for which they are paying. Were that so, we would be expected to provide the necessary training materials. In our model, participants bring along the tools they want to experiment with, to mutual benefit.<\/p>\n\n\n\n
Outcomes<\/strong><\/h2>\n\n\n\nLabs generate value in multiple forms. Not just outputs, but understanding, confidence, and practical insight. These outcomes may arise during the session or emerge through later reflection and reuse.<\/p>\n\n\n\n
Types of value include:<\/p>\n\n\n\n
\n- Practical insight<\/strong> \u2013 What AI tools help with, where they fall short, and how to work with their quirks<\/li>\n\n\n\n
- Confidence and capability<\/strong> \u2013 Participants gain familiarity, reduce anxiety, and become more willing to explore<\/li>\n\n\n\n
- Use-case refinement<\/strong> \u2013 A vague idea becomes a better-shaped task, with clearer expectations and improved prompts<\/li>\n\n\n\n
- Reusable components<\/strong> \u2013 Prompt-output pairs, templates, or notes that can feed future labs or be reused by others<\/li>\n\n\n\n
- Narrative material<\/strong> \u2013 Raw experience captured as diary posts, lab notes, or reflections on what worked and what didn\u2019t<\/li>\n\n\n\n
- Inputs for iteration<\/strong> \u2013 Insights that shape future experiments or point to promising next steps<\/li>\n\n\n\n
- Trust-building<\/strong> \u2013 Sharing missteps and progress openly builds credibility, especially for those unsure where to begin<\/li>\n<\/ul>\n\n\n\n
These outcomes matter even if the AI doesn\u2019t perform well. Failures are often the most instructive results.<\/p>\n\n\n\n
Transparency Label Justification. This canvas was developed through a structured and iterative collaboration between Alec Fearon and ChatGPT. Alec directed the purpose, structure, and terminology of the document, prompted each section\u2019s development, and critically reviewed all contributions. ChatGPT generated draft content, proposed structural changes, and refined language under Alec\u2019s supervision. The ideas and framing reflect shared authorship, with Alec clearly in charge of direction and tone.<\/p>\n\n\n\n
\n","protected":false},"excerpt":{"rendered":"Conceptual Framework for Anapoly AI Labs transparency label: AI-assisted Note: This document is an early draft, currently under review. Some of the terminology, in particular, is under debate. Our stance We don\u2019t claim to be AI experts. We\u2019re practitioners exploring AI in real problems faced by professionals like us. We\u2019re testing, documenting, and improving \u2013 […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-365","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/pages\/365","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/comments?post=365"}],"version-history":[{"count":24,"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/pages\/365\/revisions"}],"predecessor-version":[{"id":698,"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/pages\/365\/revisions\/698"}],"wp:attachment":[{"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/media?parent=365"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/categories?post=365"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/tags?post=365"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}