Empty Result Temporal Relaxation Prompt for Fallback Retrieval

This prompt is for search engineers and RAG developers who need a systematic fallback when a retrieval pipeline returns zero results due to overly strict temporal constraints. The job-to-be-done is recovering relevant documents by intelligently relaxing date ranges, removing boundary conditions, or broadening temporal windows in a controlled sequence. The ideal user is someone integrating this into a production retrieval system where an empty result set is a critical failure, not a minor inconvenience. Required context includes the original query, the strict temporal filter that produced zero results, the total document count in the corpus, and a predefined relaxation policy that specifies the order of operations (e.g., expand by 7 days, then 30 days, then remove the end date, then remove all temporal constraints).

Do not use this prompt when the empty result set is caused by a fundamentally different problem, such as a malformed query, an incorrect metadata field, or a topic with no representation in the corpus. Applying temporal relaxation to a query that failed for non-temporal reasons wastes compute and risks returning irrelevant documents that appear to satisfy the loosened constraints. This prompt is also inappropriate when the temporal constraint is a hard business requirement—for example, a compliance search for documents within a regulatory filing period where returning results outside the window would be misleading. In such cases, the correct behavior is to report the empty result set to the user or escalate, not to silently broaden the search. Before wiring this into a pipeline, ensure you have a way to distinguish temporal failures from other retrieval failures, typically by testing the query without any temporal filter first.

The prompt works best as part of a multi-step fallback chain where each relaxation step is evaluated for result count and relevance before proceeding to the next. You should define a relaxation policy as a configuration object—an ordered list of operations like EXPAND_RANGE_DAYS:7, EXPAND_RANGE_DAYS:30, DROP_END_DATE, DROP_START_DATE, DROP_ALL_TEMPORAL—and pass it into the prompt as [RELAXATION_POLICY]. The model's job is to apply the next untried step from this policy and generate the new temporal filter, not to invent its own relaxation strategy. After the prompt returns, validate that the new filter is strictly broader than the previous one and that it doesn't introduce logical contradictions like a start date after an end date. Log each relaxation attempt with the step applied, the new filter, and the result count so you can tune the policy over time. For high-stakes domains like legal or financial search, require human review before applying any relaxation beyond a configured safety threshold.

This prompt template implements a structured fallback sequence for temporal constraint relaxation. When a RAG pipeline returns zero documents for a query with strict date boundaries, this prompt instructs the model to generate a series of progressively broader temporal rewrites. Each relaxation step removes or widens one constraint at a time, preserving the original query intent while expanding the retrieval window. The output is an ordered list of fallback queries with explicit date ranges, so your retrieval system can execute them in priority order until sufficient results are found.

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You are a retrieval query rewriter specializing in temporal constraint relaxation for fallback search.

Given an original query with strict temporal boundaries that returned zero results, generate a sequence of progressively relaxed query variants. Each variant should broaden the temporal scope by exactly one relaxation step. Preserve all non-temporal aspects of the original query.

## INPUT
Original Query: [ORIGINAL_QUERY]
Original Date Range: [ORIGINAL_DATE_RANGE]
Reference Date: [REFERENCE_DATE]
Corpus Time Span: [CORPUS_MIN_DATE] to [CORPUS_MAX_DATE]

## RELAXATION STEPS (apply in order, one per variant)
1. Expand boundaries outward by [EXPANSION_INCREMENT] (e.g., 7 days, 1 month, 1 quarter)
2. Remove the end boundary (open-ended forward)
3. Remove the start boundary (open-ended backward)
4. Widen to the enclosing calendar period (day -> week -> month -> quarter -> year)
5. Remove all temporal constraints (query only)

## CONSTRAINTS
- Never generate a date range that exceeds the corpus time span.
- Never alter the non-temporal meaning of the original query.
- Each variant must include an explicit ISO 8601 date range.
- Stop generating variants once you reach a fully unbounded query.
- If the original query had no temporal constraints, return an empty list.

## OUTPUT SCHEMA
Return a JSON array of relaxation steps in execution order:
[
  {
    "step": 1,
    "relaxation_type": "boundary_expansion",
    "query_variant": "string",
    "date_range": {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"},
    "rationale": "string"
  }
]

## EXAMPLES
[EXAMPLES]

Adaptation guidance: Replace [EXPANSION_INCREMENT] with a domain-appropriate value—7 days for news, 30 days for support tickets, or 1 quarter for financial filings. The [EXAMPLES] placeholder should contain 2–3 worked examples showing the relaxation sequence for typical queries in your domain. If your retrieval system uses metadata filter syntax instead of raw date ranges, add a filter_syntax field to the output schema that generates the actual filter clause (e.g., published_at >= '2024-01-01'). For high-stakes domains like legal or compliance, add a [RISK_LEVEL] parameter that limits relaxation to boundary expansion only when set to "restricted", preventing fully unbounded fallback queries that could surface irrelevant or misleading documents.

The Empty Result Temporal Relaxation Prompt is not a standalone query rewriter; it is a fallback recovery module that activates only when a primary retrieval step returns zero results under strict temporal constraints. In a production RAG or search pipeline, this prompt sits inside a conditional branch: the system first attempts retrieval with the original, tightly bounded date range. If the result count is zero (or below a minimum threshold), the system invokes this prompt to generate a sequence of relaxed temporal constraints, then retries retrieval with each relaxation step until sufficient results are recovered or the relaxation budget is exhausted.

To wire this into an application, implement a relaxation loop with explicit controls. The prompt output should be a structured JSON array of relaxation steps, each containing a step number, a date_range object with start and end ISO 8601 strings, a relaxation_action description (e.g., expand_range_by_30_days, remove_end_boundary, drop_temporal_filter), and a rationale field. Your application code parses this array and iterates through steps sequentially. For each step, construct a new retrieval query using the relaxed date range, execute it against your search backend, and check the result count. Stop on the first step that returns results above your minimum threshold. Log every step—original query, relaxation step applied, result count, and whether the step succeeded—for observability and debugging. If all steps are exhausted without results, surface a controlled empty-state response to the user rather than silently failing.

Validation and safety checks are critical before executing relaxed queries. Validate that each relaxed date range is logically consistent (start ≤ end), does not expand beyond your corpus boundaries, and does not introduce future dates unless explicitly allowed. Implement a maximum relaxation budget—for example, no more than 5 steps or no range wider than 10 years—to prevent runaway expansion. If the prompt output is malformed or contains invalid date strings, fall back to a conservative default relaxation (e.g., drop the temporal filter entirely) and log the parse failure. For high-stakes domains like legal or financial retrieval, require that the relaxation steps include explicit assumptions in the rationale field, and consider routing the final relaxed results through a human review queue if the relaxation was aggressive (e.g., boundary removal or multi-year expansion).

Model choice and latency considerations matter here because this prompt runs on the critical path of a user-facing request after a retrieval failure has already consumed time. Use a fast, instruction-following model (e.g., GPT-4o-mini, Claude Haiku, or a fine-tuned small model) rather than a large reasoning model. The prompt should be designed for low-latency JSON output with response_format or equivalent structured output mode enabled. Cache the prompt prefix if your system uses prompt caching. Set a strict timeout (e.g., 2–3 seconds) on the relaxation generation call; if it times out, fall back to a hardcoded relaxation sequence (e.g., expand by 30 days, then 90 days, then drop filter). This ensures the fallback path itself has a fallback.

Testing this harness requires a dedicated eval suite. Build a golden dataset of queries with known temporal constraints and intentionally empty result scenarios. For each test case, verify that the relaxation steps are generated in a sensible order (narrower relaxations before broader ones), that the loop terminates correctly when results are found, and that the final retrieved documents remain temporally relevant to the original query intent. Measure the relevance preservation rate: what percentage of relaxed retrievals return documents that a human reviewer would consider acceptably relevant to the original temporal intent? If this rate drops below a threshold (e.g., 80%), tune the prompt's relaxation ordering or add a post-retrieval relevance filter. Also test edge cases: queries with impossible temporal constraints (e.g., a date range entirely before your corpus start), queries where relaxation produces thousands of irrelevant results, and queries where the original constraint was the only thing preventing noise. In that last case, the harness should still return results but flag them with a low-confidence signal to the downstream answer generator.