Inferensys

Prompt

Multi-Hop Fact Verification Query Chain Prompt

A practical prompt playbook for using Multi-Hop Fact Verification Query Chain Prompt in production AI workflows.
Developer doing prompt engineering on laptop, prompt variations visible on screen, casual coding session.
PROMPT PLAYBOOK

When to Use This Prompt

Learn when to deploy the Multi-Hop Fact Verification Query Chain Prompt and when a simpler approach will suffice.

This prompt is designed for fact-checking and verification RAG pipelines that must independently verify every component claim within a statement. It decomposes a statement into atomic claims, generates a retrieval query for each claim, and then produces cross-reference queries that validate claims against independent sources. Use this prompt when a single retrieval round cannot provide sufficient evidence to confirm or refute a multi-part statement, and when source independence is a hard requirement for the verification verdict.

This prompt belongs in the orchestration layer of a verification system, not in the final answer-generation step. It outputs a verification plan—a structured JSON payload containing claim decomposition, per-claim retrieval queries, and cross-reference queries—that downstream retrieval executors and evidence rankers consume. The prompt does not itself retrieve documents or render a verdict; it produces the query chain that makes those steps possible. Wire this into a pipeline where a separate retrieval engine executes each query, an evidence ranker scores retrieved passages, and a final synthesis step produces the verification report.

Do not use this prompt for simple fact-checking where a single retrieval round can confirm or refute the entire statement. Avoid it when latency budgets are tight and the statement contains only one verifiable claim. This prompt is also inappropriate when source independence is not required—for example, when verifying against a single trusted knowledge base. In high-risk domains such as healthcare claims verification or legal fact-checking, always route the final verification output through human review, regardless of how thorough the query chain appears. The prompt generates a plan, not a verdict, and the plan's quality depends on the retrieval engine's ability to find independent, authoritative sources.

PRACTICAL GUARDRAILS

Use Case Fit

Where the Multi-Hop Fact Verification Query Chain Prompt works and where it introduces more risk than value.

01

Good Fit: Structured Fact-Checking

Use when: you need to verify a multi-claim statement against independent sources. The prompt excels at decomposing a statement into atomic claims and generating retrieval queries that cross-validate each claim. Guardrail: always require source independence in the output schema—queries must target distinct documents or sources to prevent echo-chamber verification.

02

Bad Fit: Single-Claim or Simple Lookup

Avoid when: the input is a single factual claim answerable from one retrieval round. The multi-hop decomposition overhead adds latency and token cost without benefit. Guardrail: route through a lightweight claim classifier first. If the statement contains only one verifiable claim, skip the chain prompt and use a direct fact-checking prompt instead.

03

Required Inputs

What you must provide: a complete statement to verify, access to a retrieval backend, and a schema for the verification plan output. The prompt needs the full claim text—partial or truncated statements produce incomplete decomposition. Guardrail: validate input completeness before invoking the chain. Reject inputs under a minimum length or with unresolved pronouns that lack referents.

04

Operational Risk: Latency Amplification

What to watch: each hop in the verification chain adds a retrieval round. A statement with five claims can trigger ten or more queries when cross-validation queries are included. Guardrail: set a maximum hop budget in the prompt constraints. If the decomposition exceeds the budget, force the prompt to prioritize high-impact claims and flag lower-priority claims as unverified rather than silently dropping them.

05

Operational Risk: Claim Splitting Errors

What to watch: the model may split a compound claim incorrectly, creating sub-claims that cannot be independently verified or merging distinct claims that require different evidence. Guardrail: add a validation step that checks each decomposed claim for standalone verifiability. If a sub-claim contains unresolved references or depends on another sub-claim for meaning, reject the decomposition and request regeneration.

06

Operational Risk: Cross-Reference Contamination

What to watch: cross-validation queries may inadvertently reuse the same source or retrieve overlapping document sets, creating a false sense of corroboration. Guardrail: include explicit source diversity constraints in the prompt. Require that cross-reference queries specify different source domains, date ranges, or document collections. Log source overlap in the verification output for downstream audit.

PROMPT PLAYBOOK

Copy-Ready Prompt Template

A reusable prompt that decomposes a statement into independently verifiable claims and generates a cross-referencing query plan for multi-source fact-checking.

This template is the core instruction set for a Multi-Hop Fact Verification Query Chain. It forces the model to act as a verification planner, not an answer generator. The prompt instructs the model to first decompose a complex statement into discrete, atomic claims, then design a sequence of retrieval queries that independently gather evidence for each claim, and finally generate cross-reference queries to validate consistency across sources. The output is a structured verification plan, not a final verdict.

text
You are a fact-verification planner. Your task is to design a multi-hop retrieval plan to verify the claims within a provided statement. You must not answer whether the statement is true or false. Instead, produce a plan that an independent retrieval system can execute.

Follow these steps:
1.  **Claim Decomposition**: Break the input statement into a list of discrete, independently verifiable atomic claims. Each claim should represent a single fact that can be checked against a source.
2.  **Independent Evidence Queries**: For each atomic claim, generate one or more search queries designed to retrieve evidence from a neutral corpus. These queries must be self-contained and must not leak information from other claims.
3.  **Cross-Validation Queries**: Generate queries that explicitly check for consistency or contradiction between the evidence found for different atomic claims. For example, if Claim A states a date and Claim B states a related event, a cross-validation query should check if the dates align.
4.  **Source Independence Check**: For each query, specify the type of source that would constitute strong evidence (e.g., "primary source," "official statistics," "credible news report") and flag if the same source could potentially answer multiple queries, which would weaken cross-validation.

**Input Statement:**
[STATEMENT]

**Constraints:**
- [CONSTRAINTS]

**Output Format:**
Respond with a single JSON object conforming to this schema:
{
  "original_statement": "string",
  "atomic_claims": [
    {
      "claim_id": "string",
      "claim_text": "string",
      "independent_queries": [
        {
          "query_text": "string",
          "target_source_type": "string"
        }
      ]
    }
  ],
  "cross_validation_queries": [
    {
      "query_text": "string",
      "claims_cross_referenced": ["claim_id_1", "claim_id_2"],
      "purpose": "string"
    }
  ],
  "source_overlap_warnings": ["string"]
}

To adapt this template, replace the [STATEMENT] placeholder with the text to be verified. Use the [CONSTRAINTS] placeholder to inject domain-specific rules, such as restricting queries to a specific date range, language, or set of trusted domains. For high-stakes verification, always route the generated plan to a human analyst for review before execution. The plan's value is in its structure; a poorly decomposed claim will lead to a useless verification chain. Test this prompt with statements containing subtle contradictions to ensure the cross-validation queries are sharp enough to detect them.

IMPLEMENTATION TABLE

Prompt Variables

Inputs required by the Multi-Hop Fact Verification Query Chain Prompt. Validate each placeholder before sending to the model to prevent hallucinated claims, missing dependencies, or source contamination.

PlaceholderPurposeExampleValidation Notes

[STATEMENT]

The claim or statement to verify. Contains one or more factual assertions that may require independent evidence.

"The same gene variant linked to increased risk of Alzheimer's also appears to reduce the risk of certain cancers, according to recent studies."

Must be a non-empty string with at least one verifiable factual claim. Reject inputs that are purely opinion, subjective preference, or single-word queries. Check for compound claims that require decomposition.

[CLAIM_DECOMPOSITION_SCHEMA]

Defines the output structure for individual claims extracted from the statement. Each claim becomes a separate verification target.

{"claim_id": "string", "claim_text": "string", "entities": ["string"], "verifiable": "boolean", "requires_temporal_scope": "boolean"}

Must be a valid JSON Schema object. Each claim record must include an id, text, and verifiable flag. Schema must support arrays of claims. Validate that the schema does not collapse distinct claims into a single record.

[VERIFICATION_PLAN_SCHEMA]

Defines the output structure for the full verification plan, including query chains and cross-reference rules.

{"plan_id": "string", "claims": [ClaimDecomposition], "query_chains": [{"claim_id": "string", "hops": [{"hop_number": "int", "query": "string", "target_source_type": "string", "expected_evidence_type": "string"}]}], "cross_validation_pairs": [{"claim_a_id": "string", "claim_b_id": "string", "relationship": "string"}]}

Must be a valid JSON Schema object. Query chains must map to claim IDs from the decomposition. Cross-validation pairs must reference valid claim IDs. Reject schemas that allow orphan queries or cross-references to non-existent claims.

[SOURCE_INDEPENDENCE_RULES]

Constraints that ensure each hop in a query chain retrieves evidence from sources independent of prior hops. Prevents circular verification.

"Each hop must target a different source domain or publisher than prior hops in the same chain. No hop may query the same URL or document identifier as a previous hop."

Must be a non-empty string or structured rules object. Rules must be parseable into executable checks. Validate that rules cover domain diversity, URL uniqueness, and publisher separation. Reject rules that allow self-referencing verification loops.

[MAX_HOPS_PER_CLAIM]

Upper bound on the number of retrieval hops per claim to prevent unbounded query chains and control latency.

3

Must be a positive integer between 1 and 10. Default to 3 if not specified. Validate that the value is within range before sending. Values above 5 should trigger a review flag due to latency and cost implications.

[TEMPORAL_CONSTRAINTS]

Optional date range or recency requirements that scope all generated queries. Ensures evidence is time-bounded.

{"start_date": "2023-01-01", "end_date": "2024-12-31", "prefer_recent": true}

If provided, must be a valid JSON object with ISO 8601 date strings. Null allowed. Validate that start_date precedes end_date. If prefer_recent is true, generated queries should include temporal filters. Reject malformed date strings.

[EVIDENCE_GROUNDING_REQUIREMENT]

Specifies whether each claim must be grounded to a retrievable source, or if the model can note when evidence is unavailable.

"Every claim must have at least one source citation. Claims without retrievable evidence must be flagged as UNVERIFIED with a reason."

Must be a non-empty string. Validate that the instruction explicitly addresses the UNVERIFIED case. Reject instructions that allow claims to pass without either a citation or an explicit unverified flag.

[OUTPUT_LANGUAGE]

Language for generated queries and verification plan annotations. Queries should match the language of target retrieval corpora.

"en"

Must be a valid ISO 639-1 two-letter language code. Default to "en" if not specified. Validate against a supported language list. Reject unsupported codes that would cause retrieval mismatches.

PROMPT PLAYBOOK

Implementation Harness Notes

How to wire the Multi-Hop Fact Verification Query Chain Prompt into a production verification pipeline with validation, retries, and human review.

This prompt is not a standalone chatbot interaction; it is a structured planning step inside a fact-verification RAG pipeline. The harness receives a statement to verify, calls the LLM with this prompt to produce a verification plan (claim decomposition and cross-reference queries), executes those queries against retrieval backends, and then feeds the retrieved evidence into a downstream synthesis prompt. The harness must treat the LLM output as an executable plan, not a final answer. Validate the plan structure before any retrieval begins: every claim must have at least one retrieval query, every cross-reference query must reference at least two claims, and the output must conform to the expected JSON schema with claims and cross_reference_queries arrays.

Wire the prompt into an application with a strict JSON mode or structured output API. After receiving the plan, run schema validation using a library like Pydantic or Zod. Check that no claim is a duplicate, that each claim is a self-contained atomic fact, and that cross-reference queries explicitly mention the claim IDs they connect. If validation fails, retry once with the validation errors injected into the [CONSTRAINTS] placeholder. Log the raw plan, validation results, and any retries for observability. For high-stakes domains like legal or medical verification, route plans with low claim coverage (fewer than three claims for a complex statement) or missing cross-references to a human review queue before retrieval execution.

After the plan passes validation, execute each retrieval query against your configured backends—typically a combination of dense vector search and keyword search for source diversity. Store the retrieved documents keyed by query ID. Then feed the original statement, the verification plan, and the retrieved evidence into a separate synthesis prompt that produces the final verdict with citations. Avoid the temptation to merge plan generation and synthesis into one prompt; separation allows independent validation of the plan and prevents the model from hallucinating evidence during the planning phase. Monitor production for plans that generate queries returning zero results, as this often indicates overly specific claim decomposition or entity extraction errors that require prompt tuning.

IMPLEMENTATION TABLE

Expected Output Contract

Fields, data types, and validation rules for the verification plan JSON. Use this contract to validate the model's output before executing the query chain.

Field or ElementType or FormatRequiredValidation Rule

verification_plan

object

Top-level object must contain claims array and cross_validation_queries array

verification_plan.claims

array

Array length must be >= 1. Each element must have claim_text, sub_queries, and source_independence_score fields

verification_plan.claims[].claim_text

string

Non-empty string. Must be a distinct extractable proposition from the input statement. Parse check: no duplicate claim_text values across array

verification_plan.claims[].sub_queries

array

Array length must be >= 1. Each element must be a non-empty string representing a retrievable query. No sub_query may be identical to the claim_text itself

verification_plan.claims[].source_independence_score

number

Float between 0.0 and 1.0. Score represents how independently this claim can be verified from other claims. 1.0 = fully independent. Schema check: value must be >= 0.0 and <= 1.0

verification_plan.cross_validation_queries

array

Array length must be >= 1. Each element must have query_text and target_claim_indices fields

verification_plan.cross_validation_queries[].query_text

string

Non-empty string. Must differ from all sub_queries in the claims array. Parse check: no exact string match against any claims[].sub_queries[] value

verification_plan.cross_validation_queries[].target_claim_indices

array

Array of integers >= 0. Each index must reference a valid index in the claims array. Array length must be >= 1. Schema check: no index out of bounds

PRACTICAL GUARDRAILS

Common Failure Modes

What breaks first when generating multi-hop fact verification query chains and how to guard against it.

01

Claim Fragmentation Misses Implicit Dependencies

What to watch: The prompt decomposes a statement into surface-level claims but misses implicit dependencies between them. A claim about 'the CEO who announced the acquisition' requires resolving 'who is the CEO' before verifying the acquisition claim. Guardrail: Add an explicit dependency detection step before query generation. Require the model to output a dependency graph and flag claims that share entities, dates, or roles. Validate that no claim references an unresolved entity from another claim.

02

Cross-Reference Queries Retrieve Overlapping Sources

What to watch: The generated cross-reference queries all hit the same source or same domain, creating a false sense of corroboration. If all verification queries return results from the same press release, independent verification hasn't actually occurred. Guardrail: Add source diversity constraints to the prompt. Require each cross-reference query to target a different source type or domain. Post-retrieval, check source URL domains for overlap and flag verification plans where more than 50% of sources share a root domain.

03

Query Chain Generates Unanswerable Verification Steps

What to watch: The model generates verification queries that are too specific, too vague, or ask for information unlikely to exist in any retrieval corpus. A query like 'Find the exact revenue figure for Product X in Q3 2019 from an independent audit report' may return nothing even if the claim is true. Guardrail: Add a retrievability check. For each generated query, require the model to assess whether the information is likely to exist in a searchable source. Flag queries scored below a retrievability threshold for human review or query relaxation before execution.

04

Entity Resolution Fails Across Hops

What to watch: The same entity is referred to differently across claims ('IBM' vs 'International Business Machines Corp.' vs 'Big Blue'), and the generated queries don't normalize these references. Subsequent hops retrieve documents about the wrong entity or miss relevant evidence entirely. Guardrail: Add an entity normalization pass before query generation. Extract all named entities from the decomposed claims, resolve them to canonical forms, and inject the canonical form plus known aliases into each verification query. Validate that entity IDs are consistent across the query chain.

05

Verification Plan Omits Negative Evidence Queries

What to watch: The generated query chain only looks for confirming evidence and never searches for contradictory or disconfirming sources. This produces verification plans that are structurally biased toward confirmation. Guardrail: Explicitly require one or more disconfirmation queries in the prompt template. For each major claim, generate a query that would surface contradictory evidence if it exists. Weight negative results appropriately in the final verification output rather than treating absence of contradiction as confirmation.

06

Temporal Misalignment Across Verification Hops

What to watch: Claims reference different time periods, but the generated queries don't propagate temporal constraints. A claim about 'Q3 revenue' verified against a source from Q2 produces misleading results. Guardrail: Extract and normalize all temporal expressions from the original statement and each decomposed claim. Propagate date range filters into every verification query. Add a temporal consistency check that validates retrieved evidence falls within the claimed time window before accepting it as supporting or refuting evidence.

IMPLEMENTATION TABLE

Evaluation Rubric

Score each criterion on a 0-3 scale where 0 is unacceptable and 3 is production-ready. Use this rubric to test the Multi-Hop Fact Verification Query Chain Prompt before shipping.

CriterionPass StandardFailure SignalTest Method

Claim Coverage

Every factual claim in [STATEMENT] is extracted and assigned at least one verification query. No claim is skipped or merged into another claim.

Output misses a verifiable claim present in the input statement. A claim is paraphrased into a different meaning.

Diff the set of extracted claims against a human-annotated claim list for 20 test statements. Require 100% recall on unambiguous factual claims.

Source Independence

Each verification query targets a distinct retrieval need. No two queries are semantic duplicates or minor rewordings that would retrieve the same top-10 documents.

Two or more queries share >80% token overlap or produce identical retrieval results in a controlled test index.

Run each generated query against a fixed test corpus. Measure Jaccard similarity of top-10 result sets per query pair. Flag pairs above 0.7 similarity.

Cross-Reference Query Validity

Cross-reference queries combine two or more previously independent claims into a single retrieval that tests their joint truth. Each cross-reference query is non-redundant with individual claim queries.

Cross-reference query is a trivial concatenation of two claim queries. Cross-reference query introduces a new claim not present in the original statement.

Manually inspect cross-reference queries against the claim list. Require that each cross-reference query references at least two claim IDs from the decomposition output.

Query Specificity

Each query contains enough detail to retrieve relevant evidence without being so narrow that it returns zero results. Queries include key entities, dates, or constraints from the claim.

Query is a verbatim copy of the claim with no retrieval-oriented reformulation. Query is so vague it returns generic top-level pages.

Execute each query against a test retrieval index. Require mean reciprocal rank of a relevant document within top-10 results to be above 0.5 across a 50-statement test set.

Output Schema Compliance

Output is valid JSON matching the [OUTPUT_SCHEMA]. All required fields are present. Claim IDs are unique and sequential. Query list is non-empty.

Missing required field. Duplicate claim IDs. Query list is empty when statement contains verifiable claims. JSON parse error.

Validate output with a JSON Schema validator. Run across 100 test statements. Require 100% schema compliance. Reject any output that fails jsonschema.validate().

No Hallucinated Claims

Every extracted claim is directly attributable to a phrase or clause in [STATEMENT]. No claim is invented, inferred, or added from external knowledge.

Output contains a claim that cannot be mapped back to a specific span in the input statement. Claim adds quantification not present in the source.

Require human annotators to map each extracted claim to a source span. Flag any claim without a span mapping. Target 0% hallucinated claims on a 50-statement test set.

Verification Plan Completeness

The verification plan includes all required sections: claim decomposition, individual claim queries, cross-reference queries, and a suggested execution order. No section is truncated or omitted.

Cross-reference section is empty when statement contains multiple related claims. Execution order is missing or contradicts stated dependencies.

Check for presence of all top-level keys in the output object. Run a structural completeness check that asserts non-empty arrays for claim list and query list. Apply across 100 test statements.

Confidence and Uncertainty Handling

When a claim is ambiguous or underspecified in [STATEMENT], the output notes the ambiguity and generates queries that cover multiple interpretations. Confidence is not asserted when evidence is likely to be mixed.

Output assigns high confidence to a claim that is clearly underspecified. Ambiguous claim is treated as a single precise claim without noting alternatives.

Include 10 deliberately ambiguous statements in the test set. Require that outputs for these statements include at least one ambiguity note or multiple interpretation queries. Flag outputs that treat ambiguous claims as single precise claims.

ADAPTATION OPTIONS

Adapt This Prompt

How to adapt

Add strict JSON output schema with claim IDs, dependency tracking, and source independence constraints. Include retry logic for malformed outputs. Add eval cases with known claim coverage targets. Wire into a RAG pipeline where generated queries are executed against a retrieval index and results fed back for cross-validation.

code
Decompose the statement into discrete factual claims. For each claim, generate retrieval queries that independently verify the claim from separate sources. Output as JSON:

{
  "claims": [
    {
      "claim_id": "C1",
      "claim_text": "[EXTRACTED CLAIM]",
      "verification_queries": ["[QUERY_1]", "[QUERY_2]"],
      "depends_on": [],
      "expected_evidence_type": "[TYPE]"
    }
  ],
  "cross_validation_queries": [
    {
      "query": "[CROSS_CHECK_QUERY]",
      "validates_claim_ids": ["C1", "C2"]
    }
  ]
}

Statement: [STATEMENT]
Constraints: [CONSTRAINTS]

Watch for

  • Queries that all target the same source or domain
  • Cross-validation queries that don't actually test consistency between claims
  • Missing dependencies when one claim's truth depends on another
  • Schema drift under high claim counts or complex statements
  • Silent failures when retrieval returns empty results for a query
Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.