Inline Citation Insertion Prompt Template

Use the Inline Citation Insertion Prompt when your primary job-to-be-done is generating a fluent, readable document—such as a report, brief, or summary—where every factual claim must be immediately traceable to its source. The ideal user is a document generation engineer, analyst, or product manager who already possesses the source material and needs the model to weave verifiable citations directly into the prose. This prompt assumes you have completed the retrieval and evidence selection step beforehand; it does not search a knowledge base or decide which sources to use. Instead, it takes a set of provided source documents and a target output structure, then produces text with bracketed citation markers (e.g., [Source A, p. 4]) inserted at the sentence or clause level.

This prompt is distinct from RAG answer citation prompts. If you need to first extract citation spans from retrieved context and then map them to answer sentences, use the Citation Span Extraction for RAG Answers Prompt instead. The inline insertion prompt is also not a fact-verification tool—it will faithfully cite the sources you provide, but it will not flag contradictions between them unless you explicitly include conflict resolution instructions in the [CONSTRAINTS] block. For workflows where source reliability varies, pair this prompt with the Source Confidence Annotation Prompt Template to attach confidence scores to each source before insertion. The prompt works best with structured source inputs that include clear span identifiers, page numbers, or section references; if your sources lack these, run the Source Metadata Extraction Prompt first to normalize them.

Do not use this prompt when the generated text will undergo heavy human rewriting that could break citation alignment, or when downstream readers do not require sentence-level provenance. In those cases, a simpler bibliography or section-level source list may suffice. Before putting this prompt into production, implement a validation step that checks whether every citation marker in the output corresponds to a real source span in the input—citation hallucination is the most common failure mode. For high-stakes compliance or legal workflows, always route the output through human review and log the full prompt-input-output triple for audit. If you are ready to proceed, the next section provides the copy-ready prompt template with all required placeholders.

This template is designed for document generation teams who need every claim anchored to a specific source span. Unlike post-hoc citation extraction, this prompt instructs the model to insert bracketed references directly into the generated text as it writes. The template assumes you have already retrieved or assembled the relevant source passages and are providing them inline. Replace the square-bracket placeholders with your actual source documents, citation format specification, and output instructions before use. This is a single-turn template—all evidence must be present in the prompt context.

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You are a precise document generator. Your task is to produce a [DOCUMENT_TYPE] based on the provided source material. Every factual claim, statistic, date, name, quote, or specific assertion you make MUST be immediately followed by a bracketed citation referencing the exact source passage that supports it.

## Source Material

[SOURCE_DOCUMENTS]

## Citation Format

Use the following format for all citations: [CITATION_FORMAT]
Example: [CITATION_EXAMPLE]

## Output Instructions

1. Generate the [DOCUMENT_TYPE] using only information present in the source material above.
2. Insert a citation immediately after every sentence or clause that contains a factual claim, using the format specified.
3. If a single sentence draws from multiple sources, include all relevant citations: "[CITATION_FORMAT, CITATION_FORMAT]".
4. If you cannot find source support for a claim that the [DOCUMENT_TYPE] typically requires, do NOT invent information. Instead, insert: "[CITATION_FORMAT: No supporting source found]".
5. Do not cite sources for your own transitional language, summaries of structure, or obvious logical connectors.
6. Output only the generated [DOCUMENT_TYPE] with inline citations. Do not include commentary, disclaimers, or meta-notes.

## Constraints

- [CONSTRAINTS]

## Output Schema

[OUTPUT_SCHEMA]

To adapt this template, start by defining your citation format precisely. If you need numeric references like [1], provide a numbered source list in [SOURCE_DOCUMENTS] and specify the format as [n]. If you need author-year like (Smith, 2024), ensure each source passage includes that metadata. For legal or compliance workflows, consider requiring page and paragraph references. The [CONSTRAINTS] field is where you add domain-specific rules—for example, 'Never cite the same source more than three times consecutively' or 'Prefer primary sources over secondary summaries.' The [OUTPUT_SCHEMA] field should specify the expected structure, whether that is plain markdown, a JSON object with a content field, or a structured document format. After generating output, validate citation placement by checking that every factual sentence has at least one citation and that no citation references a source passage that does not contain the claimed fact. For high-stakes workflows, route outputs with missing or unverifiable citations to human review before publication.

The inline citation insertion prompt is designed to operate as a post-generation step in a document assembly pipeline. After your primary model generates a draft answer, summary, or report, this prompt receives the draft text and a set of pre-retrieved source spans, then inserts bracketed citations at the sentence or clause level. The prompt expects two structured inputs: the [DRAFT_TEXT] to be annotated and a [SOURCE_MAP] containing source IDs paired with their full text spans and metadata. The output is the same draft text with inline citation markers such as [S1], [S2], or [S1,S3] inserted immediately after the supported claims. This separation of generation and citation insertion keeps each step testable and allows you to swap the generation model without retuning the citation logic.

In production, wrap this prompt in a function that validates the output before it reaches downstream consumers. First, parse the returned text and extract every citation marker. For each marker, verify that the referenced source ID exists in the original [SOURCE_MAP]—hallucinated source IDs are a common failure mode. Second, run a source span validation check: for each cited claim, confirm that the referenced source span actually contains the asserted fact. This can be done with a separate validation prompt or a lightweight entailment model. Third, implement a citation completeness audit that flags sentences containing factual claims but lacking any citation marker. If the completeness score falls below your threshold (e.g., fewer than 90% of factual claims cited), route the output to a retry loop with more explicit instructions about which claims need attribution. Log every validation result—citation count, hallucinated IDs, missing citations, and span mismatch rate—so you can detect drift in model behavior over time.

For high-stakes domains such as legal, clinical, or compliance documentation, add a human review gate after automated validation passes. Present reviewers with a side-by-side view: the cited draft on one side and the corresponding source spans on the other, with each citation marker highlighted and clickable. The review interface should allow reviewers to accept, reject, or modify individual citations. Track reviewer actions to build a feedback dataset for future prompt improvements. Avoid the temptation to skip validation by relying solely on the model's self-reported confidence—inline citation insertion models frequently express high confidence on hallucinated citations. Instead, treat the prompt output as a draft annotation layer that requires deterministic checks before it becomes part of the official record.