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Martin Fowler

DSLs Enable Reliable Use of LLMs Fragments: July 13 Experiences with local models for coding Viability of local models for coding Fragments: July 6 Building Reliable Agentic AI Systems Fragments: June 16 Fragments: June 2 Fragments: May 27 The VibeSec Reckoning bliki: Vibe Coding Maintainability sensors for coding agents Fragments: May 14 What Is Code? Fragments: May 5 bliki: Mythical Man Month Fragments: April 29 Structured-Prompt-Driven Development (SPDD) Fragments: April 21 Fragments: April 14 Alan Turing play in Cambridge MA Fragments: April 9 Feedback Flywheel Principles of Mechanical Sympathy Harness engineering for coding agent users Fragments: April 2 Encoding Team Standards Fragments: March 26 bliki: Architecture Decision Record Fragments: March 19 Context Anchoring Fragments: March 16 Fragments: March 10 Ideological Resistance to Patents, Followed by Reluctant Pragmatism Ideological Resistance to Patents, Followed by Reluctant Pragmatism Humans and Agents in Software Engineering Loops Design-First Collaboration Fragments: February 25 Knowledge Priming Fragments: February 23 Fragments: February 19 bliki: Host Leadership Fragments: February 18 bliki: Agentic Email bliki: Future Of Software Development bliki: Excessive Bold My favorite musical discoveries of 2025
bliki: Interrogatory LLM
Martin Fowler · 2026-05-14 · via Martin Fowler

When we need an LLM to perform a complex task, we often need to feed it a lot of context. Coming up with a design for a new feature requires descriptions of how we want the feature to appear to the user, guidelines on how it should be implemented, information on external systems to consult, and so on. All this can be several pages of markdown. The obvious way to do this is for a human to write this context, but an alternative is to use an LLM to write this context after interviewing a human.

The way I can do this is to prompt the LLM to interrogate me. It should ask me all the questions it needs to create this appropriate context. I can feed much of the information it needs, and tell it other sources it needs to consult if it can't figure those out itself. Once it's done, it can then create the context report for another session (perhaps with another model) to carry out the next step.

I first saw a decent description of this approach in Harper Reed's blog. A striking element of his approach is insisting that the LLM ask only one question at a time. (When I tried it, I found it needed to be frequently reminded of this.)

Another way to use an interrogatory LLM is to give it a document, such as a software specification, that captures knowledge about a domain - and then ask the LLM to interview a human expert to determine if the document is accurate. This is an alternative to getting the human expert to read the document to review it. People often find reviewing hard, so a conversation with an LLM might be more fruitful, particularly if the document isn't well-written.

Naturally we can use both of these, using one interrogatory LLM to build a document, then using other interrogatory LLMs to review it with other experts.

The above is getting an LLM to create or assess context for a particular use of an LLM. But the technique is more broadly applicable. I've become a natural writer, someone who finds the process of writing an essential part of thinking. To really understand something, I need to write about it. But different people are different. Many folks find writing hard, often very hard. This can be a real problem when we need to get information out of someone's head into a form that other humans can consume. Maybe such people would find it easier to ask an LLM to interview them than to write a document themselves. Certainly the result will have that tang of AI-writing that folks like me shudder at - but that's better than not having the information itself, either due to rushed writing or no writing at all.