Conversational Context Gathering with LLMs

Introduction

When tasked with a complex assignment, a large language model (LLM) often requires extensive context to produce a useful result. This context may include feature descriptions, implementation guidelines, external system references, and more—often spanning multiple pages. The conventional approach is to have a human write all of this material. However, an alternative method is gaining traction: using the LLM itself to interview a human and record the information needed.

The Interrogation Process

In this approach, you prompt the LLM to act as an interrogator. The model asks you a series of questions to gather the necessary context. You can directly provide known facts, and you can instruct it to consult external sources for details it cannot determine on its own. Once the questioning is complete, the LLM compiles a context report. This report can then be used in a separate session—perhaps with a different model—to carry out the next stage of the task.

One Question at a Time

A key refinement of this technique comes from Harper Reed, who emphasized that the LLM should ask only one question per turn. This constraint helps keep the conversation focused and prevents the human from being overwhelmed. In practice, the LLM may need frequent reminders to stick to this rule, but the result is a more structured and manageable dialogue.

Using the Interrogatory LLM for Document Review

Another application is to give the LLM an existing document—such as a software specification—and have it interview a human expert to verify the document's accuracy. This serves as an alternative to asking the expert to read and review the text directly. Many people find reviewing documents tedious, especially if the writing is unclear. A conversation with an LLM may be more engaging and productive, leading to better identification of errors or omissions.

Combining Both Techniques

Naturally, the two methods can be combined. One interrogatory LLM can build a context document, and other interrogatory LLMs can then review that document with different experts. This creates a workflow where knowledge is captured and validated through structured dialogue rather than static reading and writing.

Broader Applications Beyond LLM Context

While the above examples focus on creating or assessing context for LLM tasks, the interrogatory LLM concept has wider utility. Many people struggle with writing. For some, writing is an essential part of thinking; for others, it is a daunting chore. When information needs to be extracted from someone’s mind and recorded for others, a conversational interview can be far easier than drafting a document from scratch. Even if the resulting text carries a noticeable “AI tang,” it is far better than having no record at all—or a hastily written one that misses critical details.

By turning the process into a dialogue, we lower the barrier for subject-matter experts to contribute their knowledge. The LLM becomes a facilitator that asks the right questions, probes for deeper explanations, and synthesizes the answers into coherent documentation. This approach respects different cognitive styles and can transform knowledge capture from a solitary burden into an interactive, efficient experience.

Conclusion

The interrogatory LLM is a powerful technique for gathering and verifying information. Whether you are preparing context for another AI task, reviewing a technical specification, or simply trying to document expertise from a colleague, letting the LLM ask the questions can save time and produce more thorough results. As the technology matures, we can expect these conversational methods to become a standard part of how humans and AI collaborate on complex projects.