In a shared AI thread, an @mention is a way to direct a message to a particular AI model rather than asking every model in the conversation to respond. It lets a team bring different models into the same workspace, target specific strengths, and keep the prompts, answers, and follow-up questions together in one visible thread.
That sounds simple, but it changes how teams can work. Instead of copying context between separate chat windows, people can ask one model to analyze a problem, ask another to challenge the analysis, and then compare their answers side by side. Used thoughtfully, @mention targeting can make research and decision-making more organized. Used carelessly, it can create extra noise or give a model less context than it needs.
This guide explains how the approach works, when to use parallel or sequential workflows, how orchestration modes support structured work, and why disagreement between models can be a useful signal—not just a problem to resolve.
What @mention targeting means
In a shared AI thread, a prompt with an @mention identifies which model should handle that request. For example, a user might write, “@Analyst, summarize the key risks,” or “@Researcher, find the assumptions we should verify.” The mention routes the request to the selected model, while the shared thread gives participants a common place to view the exchange.
The exact interface depends on the product. In some tools, a model name appears in an autocomplete menu; in others, users select a model from a list or use a command. The underlying idea is the same: the user chooses who should respond to a particular request, instead of treating every AI system as interchangeable.
That distinction matters because models can differ in strengths, response style, speed, and suitability for a task. One may be useful for generating options, another for careful critique, and another for synthesizing a long discussion. The goal is not to find one universally “best” model. It is to target specific strengths that match the work at hand.
How a shared thread works
A shared AI thread functions as a common conversation space. People can add context, direct questions to models, review responses, and ask follow-ups without moving the work across disconnected chats. Depending on the product and its settings, collaborators may also be able to see one another’s prompts and responses.
In practice, a thread may include:
- Human context: the objective, constraints, documents, or relevant background. Targeted prompts: requests addressed to a specific model with an @mention. Model responses: analysis, drafts, summaries, critiques, or recommendations. Follow-up questions: requests that clarify, test, or extend an earlier answer. Team judgment: decisions about what to verify, accept, revise, or reject.
The thread is not automatically a single, unified “brain.” A model may not see every message, file, or response in exactly the same way. Available context can vary by product, model, permissions, and thread design. Before relying on an answer, check what the model was given and whether important assumptions were stated explicitly.
How to write an effective @mention prompt
A useful @mention prompt does more than name a model. It tells that model what role to play, what material to use, and what kind of output will help. A short but specific prompt can be more effective than a long, open-ended request.
Try including four elements:
Target: Name the model or role you want to respond. Task: State the action clearly: compare, summarize, critique, calculate, or draft. Context: Identify the relevant information or point to the earlier answer to use. Output: Specify the format, level of detail, or decision criteria.For example: “@Analyst, review the acquisition assumptions above. List the five most decision-sensitive assumptions, explain why each matters, and separate facts from estimates.” This prompt gives the model a clear assignment and a useful output structure.
For an iterative exchange, ask a direct follow-up question. Rather than writing “Can you say more?”, try: “@Analyst, which two assumptions would change the valuation most if they were wrong? Show the reasoning and state what evidence would resolve each one.” A direct follow-up question helps keep the next response anchored to the decision instead of expanding the conversation in every direction.
Parallel versus sequential workflows
There are two common ways to involve multiple models: ask them to work in parallel, or ask them to work in sequence. Neither is always better. The right choice depends on whether you want independent perspectives or a chain of increasingly focused work.
Parallel workflow: get independent views
In a parallel workflow, you send substantially the same question to more than one model at about the same time. Each model develops its own answer without relying on another model’s response. This is useful when you want to compare interpretations, generate options, or identify uncertainty.
For example, a team assessing a market could ask one model for the strongest growth case and another for the biggest reasons the market may be smaller than expected. The responses can then be compared for shared assumptions, missing evidence, and areas of disagreement.
Parallel work is especially helpful when:
- You want a second opinion before settling on a conclusion. You are brainstorming alternatives and want breadth. You need to spot omissions or competing interpretations. You want to see how different models handle the same evidence.
A limitation is that parallel answers can repeat familiar points without resolving them. If you ask several models the same broad question, you may get several polished but overlapping summaries. Make the comparison valuable by asking for distinct lenses, such as “best case,” “risk case,” and “evidence gaps.”
Sequential workflow: build on earlier work
In a sequential workflow, one model’s response becomes input for a later prompt. A first model might organize the evidence; a second might challenge the assumptions; a third might turn the reviewed analysis into a concise decision memo. The sequence creates a clear progression from raw material to a more useful output.
Sequential work is useful when tasks depend on one another. It can reduce repetition and help a team move from exploration to review to synthesis. But it also introduces a risk: an early mistake or unsupported claim can be carried forward and made to look more authoritative in later responses.
To reduce that risk, ask later models to verify rather than simply accept earlier answers. For instance: “@Reviewer, examine the analysis above. Identify claims that need source verification, flag unsupported assumptions, and do not treat prior model conclusions as established facts.”
Workflow Best for Watch out for Parallel Independent opinions, alternatives, and comparison Duplicate answers or unresolved disagreement Sequential Multi-step tasks that move from analysis to review to synthesis Passing an early error along as if it were verified Hybrid Independent analysis followed by shared critique and synthesis Extra coordination if roles and handoffs are unclearOrchestration modes for structured work
Orchestration is the way a team organizes which model does what, and in what order. Even when a tool does not offer formal orchestration settings, you can create a lightweight workflow by assigning roles in your prompts. Clear roles prevent a shared thread from becoming a queue of unrelated answers.
Ask mode
Use an ask-style mode when a person wants a targeted response from one model. This is appropriate for a bounded request, such as defining a term, summarizing a document, or checking a calculation. The person remains responsible for judging the answer.
Compare mode
Use a compare-style workflow when two or more models should answer independently. Give each the same evidence and a distinct lens, or ask each to answer the same question so the team can inspect differences. Keep the original prompts visible so reviewers know whether the models received equivalent instructions.
Review mode
Use a review-style workflow when a draft or analysis needs a challenge. A reviewing model can look for missing multiai.pro assumptions, unclear logic, contradictions, or questions a decision-maker might ask. It should not be treated as a substitute for expert, legal, financial, or factual verification.
Synthesis mode
Use a synthesis-style workflow after the team has reviewed the evidence and any disagreement. Ask a model to summarize what is known, what remains uncertain, and what decision options are available. A good synthesis preserves meaningful differences rather than smoothing them into false consensus.
Whatever the mode, define the handoff. Tell each model what it should use, what it should ignore, and what it must return. A simple sequence might be: extract evidence, identify risks, ask targeted follow-ups, and then draft a decision summary with open questions clearly labeled.
Disagreement is a signal for better decisions
When models disagree, it is tempting to pick the answer that sounds most confident or to ask for a compromise. A better response is to treat disagreement as a prompt for investigation. It can reveal ambiguous wording, incomplete context, different assumptions, or a question that needs better evidence.
Suppose two models estimate an outcome differently. Instead of asking one to “win,” ask both to show their assumptions. Then compare the inputs, definitions, time horizons, and evidence behind each answer. The disagreement may disappear once the assumptions are aligned—or it may show exactly where the decision is sensitive.
Useful follow-ups include:
- “@Model A, state the assumptions behind your estimate.” “@Model B, which input differs most from the other analysis?” “@Reviewer, identify what evidence would distinguish these two conclusions.” “@Analyst, show how the recommendation changes under both scenarios.”
Disagreement is not proof that one model is right, or that the more detailed answer is more reliable. It is a cue to inspect the evidence and decide what must be verified by a person or a trusted source.
Price example: a $42M Skybridge acquisition
Consider an illustrative Skybridge scenario in which a buyer is evaluating a $42M acquisition. The price is a decision input, not a conclusion about whether the deal is attractive. A shared AI thread can help organize the questions around that number, but the analysis still depends on verified financials, transaction terms, and qualified professional review.
A structured workflow might look like this:
Frame the decision: A team member posts the $42M price, the stated deal rationale, and the information available so far. Run independent analyses: The team uses @mentions to ask one model to identify valuation drivers and another to list diligence risks. The prompts make clear that the models should work independently. Compare assumptions: The team checks whether the models use the same definitions, time period, and treatment of debt, cash, or other relevant deal terms. Ask direct follow-ups: The team asks which assumptions most affect the conclusion and what evidence is needed to verify them. Review and synthesize: A reviewer flags unsupported claims, and a final model summarizes the decision factors, unresolved questions, and next steps.The point of the example is not that AI can decide whether Skybridge is worth $42M. It is that @mention targeting can help a team divide a complex question into manageable assignments, compare perspectives, and make uncertainty visible. The decision should rest on validated inputs and accountable human judgment—not on a confident-sounding response in a chat.
Common mistakes to avoid
- Mentioning a model without a clear job: “Take a look” does not say whether you want a summary, critique, or calculation. Assuming every model sees the same context: Restate critical facts and check which documents or messages are available. Confusing agreement with accuracy: Similar answers can share the same mistaken assumption. Passing drafts forward without verification: A sequential workflow can amplify errors unless later stages are asked to check them. Asking for a consensus too early: Preserve competing views until the key assumptions and evidence have been examined. Sharing sensitive information without review: Follow your organization’s privacy, security, and data-handling policies.
A practical rule of thumb
Use an @mention when you know which model or role should handle a specific task. Choose a parallel workflow when you want independent perspectives; choose a sequential workflow when one step genuinely depends on the previous one. Add orchestration by assigning clear roles, and treat disagreement as a reason to investigate assumptions rather than as a contest to declare a winner.
The strongest shared AI threads make the work legible: who was asked to do what, what context they received, where their answers differ, and what remains unverified. When a team can see those things in one place, @mention targeting becomes more than a shortcut. It becomes a practical way to coordinate AI assistance while keeping human judgment at the center of the decision.