Assembled Prompt Reconstruction Logger Template

This playbook is for AI operations and platform engineering teams who need to debug production failures by inspecting the exact, fully resolved prompt the model received. The core job is to reconstruct the final assembled prompt—with all variables substituted, conditional blocks resolved, and context packed—and log it for post-hoc analysis. The ideal user is an engineer diagnosing a bad output, an operator investigating a cost spike, or a security reviewer auditing what data reached the model. You need this when your prompt assembly logic is complex enough that the raw template and input variables alone cannot explain the model's behavior.

Use this template when your prompt assembly pipeline involves dynamic variable substitution, conditional branching, or runtime context injection that makes it impossible to guess the final prompt from the source template. It is particularly valuable in multi-tenant systems where user roles, feature flags, or risk levels change the assembled prompt at runtime. Do not use this prompt when your assembly logic is trivial—a simple string format with two variables does not need a reconstruction logger. Also avoid it when logging the full prompt would violate data retention policies or expose sensitive customer data that should never be persisted. In those cases, pair this with a PII redaction preflight template or log only a content-hash fingerprint instead.

Before implementing, confirm that your logging infrastructure can handle the volume and size of fully assembled prompts, which can be tens of thousands of tokens per request. You should also define a sampling strategy—logging every prompt in high-throughput systems can become a storage and cost problem. Start by logging prompts only for requests that fail validation, return low-confidence scores, or trigger an escalation path. The next step is to wire this template into your prompt assembly harness so that every assembled prompt is captured alongside its trace ID, version stamp, and model response for end-to-end debugging.

The template below captures the essential instructions for reconstructing a complete model request from disparate log entries. It is designed to be copied directly into your prompt management system or codebase. Every square-bracket placeholder must be replaced at runtime by your application's assembly logic. The prompt instructs the model to act as a reconstruction engine, not to generate creative content, which keeps the output deterministic and machine-readable.

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You are an AI prompt reconstruction logger. Your only job is to output the fully assembled prompt that was sent to the model, reconstructed from the provided log fragments.

Reconstruct the prompt by resolving all substitutions, inserting all context blocks, and ordering sections according to the assembly rules described below. Output ONLY the reconstructed prompt inside a single fenced code block. Do not add commentary, analysis, or markdown outside the code block.

## Assembly Rules
1. Start with the [SYSTEM_MESSAGE].
2. Append the [USER_INPUT] exactly as provided.
3. If [TOOL_RESULTS] is not empty, append a "Tool Results" section containing each result.
4. If [RETRIEVED_CONTEXT] is not empty, append a "Context" section with each passage prefixed by its source.
5. Append the [OUTPUT_SCHEMA] section if present.
6. Append the [FEW_SHOT_EXAMPLES] section if present.
7. Resolve all variable placeholders using the [VARIABLE_MAP]. If a variable is missing, insert "[UNRESOLVED: variable_name]".
8. Redact any values matching patterns in [REDACTION_PATTERNS] by replacing them with "[REDACTED]".

## Input Log Fragments
<system_message>
[SYSTEM_MESSAGE]
</system_message>

<user_input>
[USER_INPUT]
</user_input>

<tool_results>
[TOOL_RESULTS]
</tool_results>

<retrieved_context>
[RETRIEVED_CONTEXT]
</retrieved_context>

<output_schema>
[OUTPUT_SCHEMA]
</output_schema>

<few_shot_examples>
[FEW_SHOT_EXAMPLES]
</few_shot_examples>

<variable_map>
[VARIABLE_MAP]
</variable_map>

<redaction_patterns>
[REDACTION_PATTERNS]
</redaction_patterns>

## Constraints
- Preserve exact ordering specified in Assembly Rules.
- Do not modify, summarize, or interpret any input fragment.
- Apply redaction patterns before output.
- Flag all unresolved variables.

To adapt this template, replace each placeholder with the actual data logged by your prompt assembly pipeline. The [VARIABLE_MAP] should be a JSON object mapping placeholder names to their resolved values. The [REDACTION_PATTERNS] should be a list of regex patterns or string literals for sensitive data like emails, API keys, or PII. If your system does not use certain sections, pass an empty string or [] and the assembly rule will skip it. For high-risk domains, always run the reconstructed output through a human review step before using it to diagnose failures, and ensure that redaction patterns are validated against a known set of sensitive data types before the log is stored.

This template is not a prompt you send to a model for an answer; it is a logging function that captures the fully resolved prompt string after all variable substitution, conditional branching, and context packing have completed. The primary job is to produce a lossless record of exactly what the model received, enabling you to replay, audit, and debug failures without guessing which version of a template or which set of variables was in play. Wire this logger as a synchronous side effect immediately before the inference call, and treat the logged payload as the source of truth for any downstream evaluation or incident review.

Implement the logger as a thin wrapper around your prompt assembly pipeline. After your assemble_prompt(template, variables) function returns the final string, pass that string—along with a unique trace_id, the template_version, and a sanitized copy of the input variables—to the logging function. Critical checks: (1) Validate that the logged string is non-empty and matches the expected character length of the assembled prompt. (2) Run a PII redaction pass before writing to the log, using a configurable deny-list of patterns (e.g., emails, credit card numbers, API keys) and a hash-based replacement strategy so you can still correlate values without storing raw secrets. (3) Store the log entry with a TTL that matches your retention policy, and emit a metric for log-write failures so you are alerted if the logging path breaks silently. For high-throughput systems, batch writes or use an async buffer to avoid adding latency to the inference hot path.

Once the log is in place, build a simple reconstruction test into your CI pipeline: take a known template, a set of test variables, assemble the prompt, log it, and then assert that the logged string is byte-for-byte identical to the expected output. This catches regressions in your assembly logic, template rendering, or variable substitution. Avoid logging raw user input or unredacted context directly; always route through a sanitization layer. If your application operates in a regulated domain, ensure the log store supports immutable writes and access audit trails. The next step is to connect these logs to your eval framework so every scored output can be traced back to its exact prompt, closing the loop between production behavior and offline analysis.