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Prompt

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A RocketRide transformation node that merges multiple pipeline inputs into a single enriched question before it reaches an LLM or agent.

What it does

Assembles a structured Question object from multiple pipeline inputs and emits it downstream. This is the primary mechanism for injecting retrieved documents, extracted text, and table content into a question before it reaches an LLM or agent node.

Each input lane maps to a different section of the rendered prompt. Inputs are collected as they arrive; the fully assembled question is not emitted until the node is closing, at which point all accumulated inputs are merged with the configured instructions and the enriched question is emitted on the questions lane.

If no instructions are configured, the default profile supplies a single instruction: "Please provide a detailed and helpful response to the following question:". If an error occurs during the merge, the node logs the error and the enriched question is not emitted.

The node registers as a filter with class type text and runs as a pure Python transformation (no external service or model is called).


Configuration

Lanes

Lane inLane outDescription
documents(none)Added to the ### Documents: section of the prompt
text(none)Added to the ### Context: section of the prompt
table(none)Added to the ### Context: section of the prompt
questionsquestionsCollects question inputs; emits the fully assembled question when the node closes

Fields

FieldTypeDescription
instructionsarrayMultiple instructions to add to questions before sending to LLM

Each instruction is added with the name User Instruction; when multiple instructions are configured they are numbered (User Instruction 1, User Instruction 2, and so on). A single string value is also accepted and treated as a one-element list.


Rendered prompt structure

When the question is consumed by an LLM or agent, it is rendered in this order:

### System Instructions:
1) **User Instruction**: [your configured instructions]

### Context:
1) [text or table content from text/table lanes]

### Documents:
Document 1) Content: [document content from documents lane]

### Current Task:
[question text from questions lane]

Typical use

The most common use is passing retrieved documents or extracted text alongside a question into an agent or LLM, giving it context it would not otherwise have.

RAG into an LLM:

           ┌──→ vector store ──→ documents ─┐
source ────┤ ├──→ Prompt ──→ LLM
└──────────────── questions ─────┘

Passing context to an agent:

text extractor ──→ text ──────────────────┐
vector store ───→ documents ──────────────┤
├──→ Prompt ──→ agent
source ─────────→ questions ──────────────┘

Schema

FieldTypeDescriptionDefault
instructionsarrayInstructions
Multiple instructions to add to questions before sending to LLM