What is multi-hop reasoning?

It is answering a question that no single document fully answers, by chaining facts across several. For example, finding a company's founder in one doc and that person's birthplace in another to answer where the founder was born.

Why force citations at each step?

Citations make the chain auditable. When the model names which document each fact came from, you can verify every link and catch the moment a hallucinated fact enters, instead of trusting an unsourced final answer.

What does max hops control?

It caps how many reasoning steps the model may chain. A low cap keeps simple questions from being over-thought; a higher cap allows deeper chains. The prompt asks the model to use the fewest hops that actually answer the question.

What if the documents do not contain the answer?

The generated prompt instructs the model to say so explicitly rather than fill the gap with outside knowledge or invention. Grounding the answer strictly in the provided documents is the whole point.

How is this different from a normal RAG prompt?

A normal prompt asks for an answer from context. This one enforces an explicit, cited reasoning chain — hop by hop, each grounded in a named source — which is essential when the answer requires combining facts no single chunk contains.

What is the Multi-Hop Question Prompt Builder?

Generates a prompt that instructs an LLM to identify and chain multiple reasoning hops across several documents, citing each source used at every step so the answer is traceable and verifiable. It runs free in your browser on Gera Tools, with nothing uploaded.

Multi-Hop Question Prompt Builder

Name: Multi-Hop Question Prompt Builder
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

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Multi-hop question prompt builder

Some questions cannot be answered from any single document — they require connecting facts that live in different sources. “Who was the CEO when the acquisition happened, and what was that person’s background at the time?” might need an earnings release, a press announcement, and a biography, chained together. A standard RAG prompt will retrieve a relevant chunk and try to answer from it alone; if the answer spans chunks, the model either hallucinates a bridge or silently omits it. This builder produces a structured prompt that forces the model to reason hop by hop across multiple documents, naming the source at each step.

How it works

You provide:

Source documents, each with a short label (for example, “Doc A: 2023 annual report”)
The question that requires crossing multiple sources
A maximum hop count — how many reasoning steps the chain may have
A citation format — inline [Doc A] or numbered footnotes

The generated prompt instructs the model to:

Identify which documents are relevant to the question
Chain its reasoning one hop at a time, each step stating a fact, its source document, and what that fact contributes to the answer
Ground every fact strictly in the provided documents — if no document contains the needed information, state that explicitly rather than filling the gap from training knowledge
Assemble the final answer only after the chain is complete, with all citations visible

Why the chain matters

In a normal prompt the model produces a confident conclusion, and any hallucinated fact is buried inside it. In a cited hop-by-hop chain, each fact is a specific, checkable claim linked to a named document. A wrong or invented fact shows up as a broken link — you can point to exactly which step is unsupported and which document was misread or invented.

Setting the hop count

The right hop count is the minimum number of distinct facts from separate sources that the question genuinely requires. A question that needs three documents needs at most three hops (one per document). An over-generous cap — asking for 10 hops on a 3-document question — gives the model room to manufacture intermediate steps that look like reasoning but are not grounded. Keep the cap tight: “use the fewest hops that actually answer the question” is the standard the generated prompt enforces.

What a good result looks like

A well-formed multi-hop answer for a question crossing Doc A and Doc B might look like:

Hop 1: Doc A states that the acquisition closed in Q2 2021. Hop 2: Doc B states that the CEO at that time was [name], who had previously led [division]. Answer: The acquisition closed in Q2 2021 under the leadership of [name], whose background was in [division] per Doc B.

If the model instead returns “Doc A and Doc B do not contain enough information to answer this question,” that is a correct and useful result — it tells you to retrieve better sources, not to try a different prompt.