Streaming Chunk Reassembly Prompt Template

This prompt is for streaming infrastructure teams who receive multiple truncated chunks from a model's streaming API and need to reassemble them into one valid, deduplicated, and coherent output. Use it when your application layer has collected partial payloads due to token limits, connection interruptions, or mid-stream disconnections, and you need a single call to stitch them together without manual alignment. This prompt assumes chunks may overlap, may arrive out of order, or may contain repeated boundary tokens. It is not a substitute for proper stream buffer management in your application code; it is a recovery and reassembly tool for when buffering fails or chunks must be processed asynchronously.

The ideal user is a backend engineer or platform developer who already has a collection of raw string chunks from a streaming response and needs to produce a single, clean output for downstream consumption. Required context includes the ordered or unordered chunk payloads, the expected output format, and any schema or structural constraints the final output must satisfy. Do not use this prompt when you can fix the streaming buffer logic directly, when chunks are so corrupted that no semantic overlap remains, or when the model's generation was halted by a safety filter rather than a transport interruption—those cases require different recovery strategies.

Before invoking this prompt, ensure you have captured all available chunks with their arrival timestamps or sequence identifiers if ordering matters. The prompt works best when chunks contain recognizable overlapping text that allows boundary alignment. If chunks are completely disjoint or the model switched topics mid-stream, reassembly quality will degrade. Always validate the reassembled output against your expected schema and run deduplication checks before forwarding the result to users or databases. For high-stakes workflows, log the original chunks alongside the reassembled output for auditability and debugging.

The following template is designed for streaming infrastructure teams who need to stitch together multiple truncated or partial model response chunks into one clean output. It detects chunk boundaries, removes overlapping content, and validates that the reassembled payload is structurally sound. Use this prompt when you have a sequence of text fragments from a streaming API, a multi-turn continuation, or a batch of partial generations that must be merged into a single application-ready response.

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You are an output reassembly engine. Your task is to merge multiple streaming response chunks into a single coherent, deduplicated, and valid output.

## INPUT CHUNKS
[CHUNKS]

## EXPECTED OUTPUT FORMAT
[OUTPUT_SCHEMA]

## REASSEMBLY RULES
1. Detect and remove overlapping text at chunk boundaries. If chunk N ends with the same sequence that chunk N+1 begins with, merge them without duplication.
2. Preserve the original order of chunks. Do not reorder content.
3. If a chunk is truncated mid-token, mid-word, or mid-structure (e.g., inside a JSON string, code block, or markdown fence), repair the break by completing the token or structure based on context from adjacent chunks.
4. If the final output is expected to be valid [OUTPUT_SCHEMA], validate structural integrity after reassembly. Flag any unrecoverable breaks.
5. Do not invent new content. If a gap exists between chunks that cannot be resolved by overlap detection, mark it with [UNRECOVERABLE_GAP] rather than hallucinating filler.
6. Strip any duplicate chunks that are exact or near-exact repeats of prior chunks.

## CONSTRAINTS
[CONSTRAINTS]

## OUTPUT
Return only the reassembled output. If validation fails, return a JSON object with:
- "status": "partial" or "complete"
- "reassembled_output": <the merged content>
- "unrecoverable_gaps": [<array of gap descriptions>]
- "validation_errors": [<array of schema violations>]

Adapt this template by replacing the square-bracket placeholders with your specific context. [CHUNKS] should contain the ordered sequence of partial responses, each clearly delimited. [OUTPUT_SCHEMA] defines the expected structure—this could be plain text, valid JSON matching schema X, markdown document, or Python code. [CONSTRAINTS] allows you to inject additional rules such as maximum output length, forbidden content patterns, or domain-specific formatting requirements. For high-stakes applications where the reassembled output feeds directly into downstream systems, add a post-processing validation step in your application harness rather than relying solely on the model's self-reported validation status.

The streaming chunk reassembly prompt is designed to sit inside a real-time ingestion pipeline, not as a standalone chat interaction. When a streaming model response is interrupted—by a network drop, a backend timeout, or a mid-stream token limit—your application receives multiple partial chunks that must be stitched into a single, valid payload. This harness describes how to call the prompt programmatically, validate its output, and decide when to retry, escalate, or accept the result. The prompt expects three inputs: a list of received chunks in order, the expected output schema or format description, and any task-level context that was present in the original generation request. Without the original task context, the model cannot distinguish between intentional repetition and accidental overlap.

Wire the prompt into your application as a post-interruption recovery step. After your streaming client detects a disconnection or an incomplete termination reason (e.g., finish_reason: 'length'), collect all successfully received chunks into an ordered array. Pass this array as [CHUNKS], the original system prompt or task description as [TASK_CONTEXT], and the expected format (e.g., 'valid JSON object with fields: id, summary, items[]') as [OUTPUT_SCHEMA]. The prompt returns a single reassembled output. Before accepting it, run three validation gates: (1) a structural parse check—does the output match the expected format? (2) a deduplication check—are there no repeated sentences, paragraphs, or JSON keys that appear identically in adjacent chunks? and (3) a boundary-alignment check—does the reassembled text flow continuously across every original chunk boundary without a semantic jump or dropped content? If any gate fails, retry once with the same inputs plus the validation error message appended to [CONSTRAINTS]. If the retry also fails, log the chunks and the failed output, then escalate to a human reviewer or a fallback model with a larger context window.

For high-throughput systems, implement a lightweight pre-check before calling the reassembly prompt. If the final received chunk ends with a complete, valid structure (e.g., a closed JSON object or a terminal punctuation mark followed by a newline), and the finish reason was stop rather than length, skip reassembly entirely—the stream completed normally. Only invoke this prompt when finish_reason indicates truncation or when your client-side buffer contains an uneven number of braces, brackets, or unclosed strings. Log every reassembly attempt with the chunk count, total character length, validation results, and latency. These logs become essential for tuning retry budgets and identifying upstream model or infrastructure issues that cause frequent truncation. Avoid calling this prompt on chunks that contain PII or sensitive data without first redacting or encrypting the chunk payloads in transit and at rest.