Participant Techniques

The three-layer setup

Before starting any substantive conversation, declare what you know, what you do not know, and what you do not know you do not know. This gives the model a map of your uncertainty, not just your objectives, and surfaces blind spots early rather than late in the conversation.

Assumption disclosure standing instruction

Add a standing rule to your Claude Project asking it to disclose all assumptions it is currently working with before proceeding. Models fill gaps silently by default. This instruction forces those assumptions into the open where you can correct them before they compound.

Database-first context loading

For technical work, especially SQL, front-load the conversation with the full database structure, table relationships, and validation tests before asking any questions. The quality of every output is bounded by the context you start with.

Organizational context brief

Open new project conversations by including organizational context, an explicit request for direct and critical feedback, and relevant project files before stating the task. The model behaves differently when it knows you want hard pushback rather than validation. Set that expectation at the top.

Markdown handoff for fresh starts

When a conversation gets long or stale, ask Claude to generate a handoff summary in markdown, capturing what matters and discarding the clutter. Drop that file into a new conversation within the same Project. You get a clean start with crystallized context and none of the accumulated drift. This technique became the foundation of the Module 2 conversation architecture unit.

Branch signal recognition

There are four signals that a conversation needs a branch or a fresh start: the conversation feels circular and stale, with no forward movement; you find yourself coaching the model's behavior within the conversation itself; the model is agreeing with everything and not surfacing problems with your reasoning; or you are growing frustrated instead of making progress. Any one of these is enough to stop, do a summary checkpoint, and start fresh.

Adversarial prompting: "now be a skeptic"

After building a proposal, plan, or argument collaboratively with AI, instruct the model to switch roles and attack what you just built together. Because it helped construct the work, it knows exactly where the weak points are. Andy applied this to a business development proposal and surfaced objections he would not have found on his own.

The meta-learning prompt

After reaching a useful outcome, ask the model to evaluate the conversation itself, assign a grade, identify what worked and what did not, and offer a better prompt you could have used. This turns every session into a prompting lesson. Dawg took the model's suggested prompt to Perplexity, which then built a four-criteria weighted analytic rubric from it.

Source-citation standing instruction

Tell Claude to bold or notate any numerical data in the output with its source at the point of generation, so trust verification is built into the output rather than chased afterward. Instead of auditing a finished document, you can see at a glance which figures are sourced and which are synthesized. Vinnie framed it as speeding up the trust-and-verify process by making sourcing visible where it matters most.

The detailed setup frame, an RFP proof of concept

Craig put the setup frame discipline to a direct test on a live RFP proposal, deliberately and intentionally more detailed in the setup, specifying the audience, who would review it, the goal, pricing parameters for travel, and other constraints. The result was a 17-page document on the second output that needed only minor tweaks. The time investment in the setup frame paid for itself in reduced iteration cycles.

The principle: describe who you are, who the audience is, what you are trying to accomplish, the domains you want covered, and any hard constraints like length or format. The gap between a bare request and a full setup frame is not a small improvement. It is the difference between a draft you rework three times and one you ship.

Reddit as a primary research source

When researching a company, competitor, or industry topic, explicitly instruct the model to search Reddit alongside conventional sources. Reddit surfaces employee perspectives, internal frustrations, and candid information that organizations are slow to find and remove. CIOs and marketing teams rarely monitor the platform with enough speed to scrub what surfaces there. Vinnie raised this at the close of a session as a specific research discipline: when you want unfiltered signals on a company or topic, Reddit is often where they live.

Flag, then spot-check

When Claude extracts data from PDFs with visually complex charts, particularly ones with diagonal or rotated data labels, ask it to flag where the label angles or chart formatting may have thrown off its readings. Then spot-check those values against the source. This turns a silent error into a flagged, manageable one, and the same move works as a sampling method for anything that runs without you: have it name where it may be wrong, and check there first.

Forcing both sides on a binary decision

When Claude started agreeing with everything on an automation decision, answering "you're right" to every prompt, Seth pushed back explicitly: give me both sides of the fence. Why should I automate this? Why shouldn't I? That reframe produced the analysis the conversation had stopped providing. When the model stops pushing back, make it push back by design.

The autonomous agent scoping test

When deciding what to hand to an agent first, pick the task where the trigger is easy to identify, the failure is easy to detect, and getting it wrong carries low consequence. Starting there keeps you from automating too much before you know how an agent behaves under real conditions.

Creator, critic, rebuild

Ask a model for its best work on a problem. Then have it critique that work as a hostile competitor looking for every weakness, gap, and bad assumption. Then give it both the original and the critique and ask it to rebuild from scratch, with the objections built in. This turns the informal back-and-forth into a three-step sequence, and the rebuild is the step most people skip.

Architecture visualization

Ask Claude to draw the current architecture of a workflow, configuration, or connected system as an HTML page: what it pulls from, how the pieces connect, and what it produces. Complexity that felt manageable in prose becomes visible as a diagram, and redundancy across the tools you touch becomes obvious.

The confidentiality discipline

Jennifer is Co-Founder at Andus Labs and brings a legal background to the program. Her guidance, which is not legal advice, is worth carrying into every cohort.

The core issue is that when employees use AI tools, including free tiers that train on user inputs, they may be exposing confidential company information whether they intend to or not. This is not a hypothetical risk. It is an active compliance and fiduciary concern, and it is why having an AI governance framework in place is something every company needs.

An NDA between participants does not solve the problem, because your company almost certainly did not authorize you to share that information outside the company, even in a trusted group setting like this one.

The practical guidance: any problem you bring to collaborative work should be one you can describe without including information your company would consider confidential. In many cases the real problem can be abstracted. You can teach the model and your peers what you are trying to solve without disclosing the underlying data, the client, or the internal context that surrounds it. This is not a reason to avoid the work. It is a discipline that makes the work more rigorous.

Evaluating AI providers as governance decisions

Choosing which AI tool to use is a governance decision. The platform you use determines whose terms you operate under, how your data is handled, and whether the company behind the tool has made enforceable commitments to the principles it claims to follow.

Jennifer's approach when evaluating a provider is to look past the marketing and ask whether the company's structure creates real accountability. Is there a published acceptable use policy? Does the company's legal structure, a public benefit corporation for instance, create obligations that a standard for-profit does not? Has the company published research on how its models behave, not just how they are intended to behave?

Her framing on Anthropic specifically: because it publishes its system cards, operates as a PBC, and makes its safety research public, it has put its commitments in writing where they can be evaluated and challenged. That is a different category of trust than marketing language.

The principle generalizes. Before building a workflow around any AI tool, spend fifteen minutes on the company's documentation: what they have written down, and what they have left out, tells you how they think about their users.