Token Budget Allocation Prompt for Filtered Context

This prompt is for AI engineers and RAG pipeline builders who need to programmatically enforce a hard token limit on the context window before answer generation. The job-to-be-done is not just filtering noise, but making an explicit, auditable trade-off: given a fixed budget and a set of retrieved passages, which ones earn their token cost? The ideal user is someone running a cost-sensitive or latency-sensitive production RAG system where context stuffing leads to degraded answer quality, higher API bills, or timeouts. You have already retrieved and possibly deduplicated a set of passages, and now you need a final, budget-compliant assembly.

You should use this prompt when the token budget is a hard constraint driven by a model's context limit, a cost ceiling, or a latency SLA. It is particularly valuable when retrieval returns a large, variable-sized set of chunks and you cannot rely on a simple top-k cutoff, because relevance scores alone don't account for information overlap. The prompt forces the model to justify its allocation decisions, which gives you a structured artifact for evaluation, debugging, and cost attribution. Required inputs include the query, the set of candidate passages with their source metadata, and the exact token budget. The output is a curated context assembly that fits within the budget, along with a per-passage allocation decision and a justification.

Do not use this prompt as a substitute for a proper retrieval step. If your upstream retrieval is returning mostly irrelevant results, fix retrieval quality first—this prompt allocates budget among viable candidates, it does not salvage a broken search pipeline. Also avoid using it when the token budget is so generous that nearly all passages fit; in that case, a simpler deduplication or relevance filter is more cost-effective. For high-stakes domains like healthcare or legal review, the allocation justification must be reviewed by a human before answer generation, because an incorrect exclusion of a critical passage can lead to an incomplete or misleading answer. The next step after reading this section is to copy the prompt template, wire it into your context assembly harness, and define the eval criteria that will tell you if the budget was allocated correctly.

The prompt below accepts a set of retrieved passages and a target token budget, then produces a budget-compliant context assembly with allocation justification. It is designed for cost-sensitive RAG deployments where every token counts and retrieval often returns overlapping or low-signal chunks. Use it as a pre-generation filter before answer synthesis to reduce downstream costs and improve signal density.

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You are a context assembly specialist. Your task is to allocate a fixed token budget of [TOKEN_BUDGET] tokens across a set of retrieved passages. Prioritize passages that contain unique, high-signal information relevant to the query. Trim or exclude low-value text such as boilerplate, redundant content, navigation elements, and off-topic material.

## INPUT
Query: [QUERY]
Retrieved Passages:
[PASSAGES]

## CONSTRAINTS
- Total output must not exceed [TOKEN_BUDGET] tokens.
- Preserve answer-critical evidence. If removing a passage would make the query unanswerable, flag it.
- Prefer information diversity. If two passages convey the same fact, keep the most authoritative or complete version.
- Do not fabricate, summarize, or rewrite passage content. Only select, trim, and reorder.

## OUTPUT_SCHEMA
Return a JSON object with the following fields:
{
  "allocated_context": "string (the assembled context within budget)",
  "total_tokens_used": number,
  "passages_included": [
    {
      "passage_id": "string",
      "tokens_allocated": number,
      "inclusion_rationale": "string (why this passage was kept)"
    }
  ],
  "passages_excluded": [
    {
      "passage_id": "string",
      "exclusion_reason": "string (redundant|low_signal|off_topic|budget_constraint)",
      "risk_note": "string or null (flag if exclusion risks answer completeness)"
    }
  ],
  "coverage_assessment": "string (brief note on whether the allocated context is sufficient to answer the query)"
}

## EXAMPLES
[EXAMPLES]

## RISK_LEVEL
[RISK_LEVEL]

Adapt this template by adjusting the output schema to match your downstream answer generator's expected format. If your pipeline uses structured passage objects with metadata, extend passages_included and passages_excluded to carry source URLs, timestamps, or authority scores. For high-stakes domains, set [RISK_LEVEL] to high and add a human review step before the allocated context is passed to answer generation. Always validate the JSON output against the schema before proceeding—malformed allocations should trigger a retry or fallback to a simpler truncation strategy.

The Token Budget Allocation Prompt is designed to sit between retrieval and answer generation in a RAG pipeline. It accepts a set of retrieved passages and a target token budget, then produces a budget-compliant context assembly with allocation justification. In production, this prompt should be called as a dedicated preprocessing step—not folded into the answer generation prompt—so that budget decisions are auditable and the downstream generator receives clean, pre-filtered context. The prompt expects two required inputs: a list of retrieved passages with metadata (source ID, retrieval score, passage text) and a target token budget expressed as an integer. Optional inputs include a query string for relevance-aware allocation and a priority hint (e.g., 'prefer recency' or 'maximize entity coverage') that influences the allocation strategy.

Wire this prompt into your application with a dedicated service function that: (1) accepts the retrieval results and budget parameter, (2) formats the prompt with the passage list and budget, (3) calls the model with response_format set to the expected JSON schema, (4) validates the output structure, (5) checks that the total allocated tokens do not exceed the budget, and (6) passes the filtered context to the answer generation step. For validation, implement a schema check that verifies the output contains allocated_passages (array of passage objects with passage_id, allocated_tokens, and retained_text fields), total_allocated_tokens (integer), and allocation_justification (string). Add a budget compliance check: if total_allocated_tokens exceeds the requested budget by more than 5%, reject the output and retry with an explicit error message. Log every allocation decision—including which passages were trimmed or dropped—so you can tune the budget threshold and debug cases where critical evidence was lost to over-aggressive trimming.

For retry logic, implement a two-stage recovery pattern. On the first failure (schema violation or budget overrun), retry with the same prompt plus a concise error description appended to the system message: 'Previous output failed validation: [specific error]. Please correct and return valid JSON within the budget.' If the second attempt also fails, fall back to a simpler allocation strategy—either truncating passages evenly to fit the budget or passing the top-N passages by retrieval score without LLM-based allocation. This prevents pipeline stalls when the model produces malformed outputs. For model choice, prefer models with strong JSON mode support (GPT-4o, Claude 3.5 Sonnet) and set temperature=0 to maximize allocation consistency. Avoid using this prompt with models under ~7B parameters unless you've validated their ability to follow the allocation schema reliably; smaller models often struggle with the counting and trade-off reasoning required. If cost is the primary concern, consider running this prompt only when the raw retrieval set exceeds the budget by more than 20%—below that threshold, simple truncation is cheaper and nearly as effective.

For monitoring and eval, track three key metrics: budget compliance rate (percentage of allocations within 5% of target), evidence retention rate (whether answer-critical passages survived allocation, measured against a labeled eval set), and allocation latency (p50/p95 in milliseconds). Build a regression test suite with 20–30 retrieval sets of varying sizes and redundancy levels, each paired with a target budget and a gold-standard set of passages that must survive allocation. Run this suite on every prompt change. For high-stakes deployments where evidence loss could cause incorrect answers, add a human review step: when the allocation justification indicates that passages were dropped due to budget constraints, flag the case for review if the dropped passages had retrieval scores above a configurable threshold. This catches cases where the model made a poor trade-off that a human would catch. Finally, cache allocation results keyed by (passage_hash, budget, query_hash) to avoid recomputing allocations for identical retrieval sets—this is especially valuable in high-traffic RAG systems where the same documents are retrieved repeatedly for similar queries.