How Expert Persona Prefixing—and Question Expansion—Supercharge LLM Tool Calling for Deep Research

Today’s most advanced LLM-powered research agents operate far beyond simple plugins or keyword-based search utilities. Instead, they orchestrate sophisticated toolchains, where the “tools” invoked may themselves be autonomous AI agents—each capable of advanced reasoning and action.

Consider a typical workflow for a deep research agent handling a complex query: Rather than issuing a one-shot search, the agent might delegate tasks to specialized, API-driven subagents, such as Perplexity.ai or a custom synthesis engine. These subagents often leverage their own large language models, deploying ReAct-style iterative loops. Within each execution cycle, they recursively invoke and coordinate a range of capabilities—search, multi-document summarization, data extraction, filtering, aggregation, and synthesis—across multiple steps to construct more robust answers.

This multi-agent, deeply recursive paradigm isn’t limited to language processing or web APIs. LLM research agents are now frequently augmented with browser automation: the agent can direct a controlled browsing environment, combining actions like scripted clicks and navigation with computer vision modules to directly interpret dynamic websites. Other agents are enhanced with symbolic reasoning engines—for instance, by converting natural language rules to Prolog, running logic-based inferences, then explaining the outcomes back to the user in fluent, accessible language.

Furthermore, LLMs with integrated code execution can generate and run custom Python for dynamic tasks—ranging from trivial counts (“how many ‘r’s are in ‘Strawberry’?”) to ad hoc statistical analyses or real-time data manipulation. The fusion of step-wise language reasoning, recursive task decomposition, heterogeneous tool invocation, and robust system integration means that modern AI research agents do more than retrieve information: they solve problems with depth, adaptability, and precision not previously possible.

Yet, not all research agents are created equal. Many existing implementations still rely on naïve strategies—issuing shallow, keyword-based queries to simple web_search tools that assemble results from major search engines and summarize them, treating the web as a vanilla RAG (Retrieval-Augmented Generation) database. High-performance agents, in contrast, take the next step: their “web_search” isn’t a single function call, but a dynamic, agentic process that leverages multiple reasoning passes, interleaves API calls, and actively expands the search based on intermediate findings—for example, by using a system like Perplexity.ai as a composable building block.

However, even the best search engine or agentic toolchain is only as good as the prompts it receives. One surprisingly effective—yet underutilized—strategy can dramatically improve both the quality and relevance of research agent outputs:

1. Prefix your query with a vivid description of the ideal world-class expert you want answering it.

2. Thoroughly expand and structure your question, making sure it captures all facets of your actual information need.

By shaping your input this way, you equip your deep research agent to reason, search, and synthesize at the level of a true domain expert—unlocking the full potential of these next-generation AI systems.

Why Prefix with an Expert Persona?

Even when LLM-powered research assistants or agents have sophisticated capabilities, their default behavior is often to treat every query equally—no matter your intent, audience, or background knowledge desired. By instructing the tool to “become” an internationally recognized expert in the query’s domain, you activate the model’s internal mechanisms for adopting tone, rigor, terminology, and reasoning depth appropriate to the field. This often leads to:

- More focused and relevant search results

- Evidence-based synthesis (not superficial answers)

- Professional language and trustworthy citations

- Awareness of current debates, standards, and best practices within the field

Example:

Regular query:

randomized controlled trials on antioxidant vitamins (C, E) for BPPV management        

How to Transform Your Query:

Write 3 sequential sentences describing the characteristics of you the perfect global renowned expert who can answer the user's question below. Response must be a single paragraph with less than 3 sentences and in second person starting with You are a...

```User's Question
{user_question}
```        

Persona-prefixed query:

You are a top-tier neurologist and vestibular specialist recognized worldwide for your expertise in balance disorders, including benign paroxysmal positional vertigo (BPPV). You have a comprehensive understanding of the latest research on nutritional influences and vitamin supplementation for vestibular health. Your evidence-based guidance helps patients and clinicians optimize treatment regimens for BPPV using the most current and reliable scientific data.          

Go Further: Expand and Clarify Your Question

While persona prefixing elevates the expertise embodied in the answer, you can go a step further by systematically expanding your question into a detailed, organized inquiry. This approach prompts the LLM (and its downstream tools) to cover every angle of your request, reducing the chance of missed crucial details or ambiguous interpretations.

Regular query:

randomized controlled trials on antioxidant vitamins (C, E) for BPPV management        

How to Transform Your Query:

**Task:**  
Transform the original user question (provided below) into a detailed and organized paragraph that thoroughly represents its intent, scope, and context.

**Instructions:**  
1. Identify the main objective(s) of the query, addressing the Who, What, When, Where, How, and Why as appropriate.
2. If the query covers multiple topics or issues, break it down into sub-questions providing essential details and alternative reasoning paths for answering.
3. Add clarifying questions to resolve any ambiguities.

**Output Format:**  
Rewrite the user’s original query in first-person perspective as a single, coherent paragraph. Ensure it comprehensively expresses your intentions, organizes all details (including sub-questions), and incorporates clarifying questions for anything uncertain.

```User's Question
{user_question}
```        

Expanded detailed version:

I am seeking detailed information on the use of antioxidant vitamins, specifically vitamins C and E, in the management of benign paroxysmal positional vertigo (BPPV). My primary goal is to find evidence from randomized controlled trials (RCTs) that evaluate the effectiveness and safety of vitamin C and vitamin E supplementation for BPPV treatment or prevention. I would like to know details such as the study design, population characteristics, intervention protocols (including doses and duration), outcomes measured, and the overall results of these trials. Additionally, it would be helpful to understand whether these trials have assessed the long-term impacts and potential side effects of vitamin use in BPPV patients. If there is a lack of high-quality RCTs in this area, please clarify this and suggest whether any relevant observational studies exist or if there are current trials underway.         

Bringing it all together

Instead of sending a regular keyword based query to your API web research tool, send a super-powered query.

Regular query:

randomized controlled trials on antioxidant vitamins (C, E) for BPPV management        

Super-powered query:

You are a top-tier neurologist and vestibular specialist recognized worldwide for your expertise in balance disorders, including benign paroxysmal positional vertigo (BPPV). You have a comprehensive understanding of the latest research on nutritional influences and vitamin supplementation for vestibular health. Your evidence-based guidance helps patients and clinicians optimize treatment regimens for BPPV using the most current and reliable scientific data.  

I am seeking detailed information on the use of antioxidant vitamins, specifically vitamins C and E, in the management of benign paroxysmal positional vertigo (BPPV). My primary goal is to find evidence from randomized controlled trials (RCTs) that evaluate the effectiveness and safety of vitamin C and vitamin E supplementation for BPPV treatment or prevention. I would like to know details such as the study design, population characteristics, intervention protocols (including doses and duration), outcomes measured, and the overall results of these trials. Additionally, it would be helpful to understand whether these trials have assessed the long-term impacts and potential side effects of vitamin use in BPPV patients. If there is a lack of high-quality RCTs in this area, please clarify this and suggest whether any relevant observational studies exist or if there are current trials underway.         

Why Does This Dual Approach Work?

Modern LLM-based agents and search tools utilize your entire prompt—not just a few keyword—both when interpreting what you want and when synthesizing a relevant, nuanced answer. By explicitly expressing your expert expectations and fully unfolding your question, you:

- Prime the system’s reasoning mode for depth and nuance.

- Clarify ambiguous terms or intent up front, saving time on back-and-forth clarifications.

- Increase answer coverage—models are more likely to surface all relevant studies, highlight evidence gaps, and offer summary reasoning.

- Eliminate unnecessary follow-up prompts by supplying disambiguation points.

- Leverage “context stitching”—especially for agent frameworks that plan multi-tool actions—so follow-up sub-queries are anticipated.

Conclusion

Getting the best from LLM-driven research agents or API-integrated tools is not just about what you ask, but how you ask it.

Prefixing your request with an expert persona primes depth and authority. Expanding your question clarifies your goals, context, and scope.

Combine these techniques and you turn every search into a consultation with a world-class expert—one who anticipates your needs, addresses gaps, and leverages all available evidence.

Next time you need clarity from your LLM, don’t just ask—describe your expert and tell the whole story of what you want to know. The results will speak for themselves.