Behavioral Contract Renewal Prompt

This prompt is for AI engineers and platform teams running long-lived, stateful assistant sessions where the initial system prompt—the behavioral contract defining tone, scope, refusal policy, and output format—is at risk of being truncated by the context window. The core job-to-be-done is to regenerate a compressed, functionally equivalent version of that contract before it scrolls out of the active context, preventing instruction drift, tone erosion, and policy violations that emerge when the model loses its foundational rules. The ideal user is a developer or operator who already has a stable, tested system prompt and needs a programmatic way to renew it mid-session without manual re-engineering.

Use this prompt when your application maintains sessions exceeding 50-70% of the model's context limit, when you observe outputs gradually diverging from the initial policy, or when you implement a context-budgeting system that proactively prunes older turns. It is specifically designed for assistants with explicit behavioral constraints—brand voice, compliance boundaries, structured output schemas, or refusal policies—where consistency across hundreds of turns is a product requirement. The prompt assumes you have access to the original system prompt and a sample of recent assistant behavior to use as compression anchors.

Do not use this prompt for short-lived sessions, single-turn workflows, or assistants without strict behavioral contracts. It is not a replacement for proper session summarization or memory management; it only renews the assistant's operating rules, not the conversation history. Avoid using it when the original system prompt is poorly structured or ambiguous—compression amplifies ambiguity. If your assistant's behavior is governed by multiple conflicting policies, resolve those conflicts first using a Policy Conflict Resolution Prompt before attempting renewal. For regulated domains, always pair this prompt with a human review step and an Instruction Re-Injection Trigger Prompt to validate that the compressed contract preserves all compliance-critical clauses.

This template produces a compressed but complete renewal of the assistant's behavioral contract when the original system prompt has scrolled out of the context window. It is designed for long-running sessions in regulated, brand-sensitive, or compliance-heavy deployments where instruction drift over many turns creates consistency and risk problems. The prompt instructs the model to extract and re-state the active behavioral contract—tone, scope, refusal policy, output format, and role boundaries—from the conversation history, optimized for minimal token cost while preserving enforceability.

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You are reviewing a long-running assistant session. The original system prompt containing the assistant's behavioral contract has scrolled out of the context window. Your task is to reconstruct a compressed but complete behavioral contract renewal from the conversation history.

[CONVERSATION_HISTORY]

Extract and re-state the assistant's active behavioral contract. The renewal must cover:
- Role definition and scope boundaries
- Tone, formality, and persona constraints
- Output format and structure requirements
- Refusal policy and disallowed content categories
- Tool-use authorization and limits
- Compliance and regulatory constraints observed in the session
- Citation and evidence-grounding requirements
- Uncertainty expression and confidence calibration rules
- Correction and error-recovery behavior

Produce the renewal in this structure:

BEHAVIORAL_CONTRACT_RENEWAL:
[Concise restatement of the full behavioral contract, optimized for token efficiency. Use imperative language. Include only rules that are actively enforced in the session. Omit rules that have never been triggered.]

DRIFT_WARNINGS:
[List any behaviors observed in the conversation that deviate from the inferred contract, with turn references. If none, state "No drift detected."]

PRIORITY_ASSERTIONS:
[If multiple policies appear to conflict, state the resolved priority order with rationale. If none, state "No conflicts detected."]

RENEWAL_TOKEN_COUNT:
[Approximate token count of the renewal block.]

[CONSTRAINTS]
- Do not invent policies not evidenced in the conversation history.
- Prefer compression over completeness when a rule has never been relevant.
- If the conversation history is insufficient to infer a policy dimension, mark it as UNOBSERVED rather than guessing.
- The renewal must be usable as a drop-in system prompt for the next turn.
- For [RISK_LEVEL] HIGH or CRITICAL sessions, flag any UNOBSERVED dimensions that are mandatory for compliance.

To adapt this template, replace [CONVERSATION_HISTORY] with the full or summarized conversation turns from the session. Set [RISK_LEVEL] to LOW, MEDIUM, HIGH, or CRITICAL based on your deployment's regulatory and safety requirements. For HIGH or CRITICAL sessions, add a human review step before the renewal is injected as the new system prompt. Validate the output by checking that all required policy dimensions are addressed and that DRIFT_WARNINGS are actionable. If the renewal exceeds your context budget, re-run with a stricter compression instruction or prune UNOBSERVED dimensions that are not mandatory for your use case.

The Behavioral Contract Renewal Prompt is not a standalone chat interaction—it is a background maintenance operation that runs inside a long-lived assistant session. The primary integration point is a context window budget monitor. When the application detects that the original system instructions are within N turns of being truncated (or when a token counter shows the initial prompt block has fallen below a safety threshold), the renewal prompt is triggered. The output is a compressed behavioral contract that is re-inserted at the top of the context window, replacing or augmenting the original instructions. This is a read-modify-write pattern on the assistant's active policy state.

Wire the prompt into your application with these concrete steps: (1) Trigger detection. Use a tokenizer-aware context tracker that monitors how many tokens remain from the original system prompt block. Set a threshold—typically when fewer than 20% of the original instruction tokens remain in the active window. (2) Input assembly. Collect the original behavioral contract, the last K assistant turns (for drift detection), and any policy violation logs from the session. Feed these into the [ORIGINAL_CONTRACT], [RECENT_ASSISTANT_TURNS], and [POLICY_VIOLATION_LOG] placeholders. (3) Model selection. Use a fast, instruction-following model (GPT-4o-mini, Claude Haiku, or Gemini Flash) rather than a large reasoning model. This is a compression and fidelity task, not a reasoning task. (4) Output validation. Parse the generated contract and validate that all required sections are present: tone constraints, scope boundaries, refusal policy, output format rules, and any domain-specific compliance statements. Use a structural validator that checks for section headers and minimum content length per section. If validation fails, retry once with the validation errors appended to the prompt as [PREVIOUS_FAILURE_REASON]. (5) Re-insertion. Replace the original system instructions with the renewed contract, but preserve a hash or version identifier of the original contract so you can detect drift between the original and the renewal over many cycles. (6) Logging. Record the renewal event with: session ID, token count at trigger, renewal latency, validation pass/fail, and a diff between the original and renewed contract for auditability.

Failure modes to handle in code. The most common failure is section omission—the model drops a required policy section to save tokens. Your validator must catch this and trigger a retry. The second is policy mutation—the model subtly changes a refusal boundary or tone rule during compression. Mitigate this by running a pairwise comparison between the original and renewed contract using a lightweight eval prompt that flags semantic differences. If the diff score exceeds a threshold, escalate to a human reviewer or fall back to re-inserting the original contract with a truncation warning. The third is renewal cascade—if the renewal prompt itself consumes enough tokens to push other context out, you may trigger another renewal immediately. Prevent this by reserving a fixed token budget for the renewal output and rejecting any generated contract that exceeds it. Do not run this prompt on every turn; it is a periodic maintenance operation, not a per-request middleware. Wire it into a background job or a context-window hook that fires at most once per N turns.