1. Prompt ID

Assign a unique ID per prompt invocation (e.g., UUID or hash). This enables traceability across retries, evaluating prompt performance over models or versions, and diagnosing hallucination clusters.
Why it matters: Enables linking downstream issues to specific prompts.

2. User ID or Session Context

Even anonymized, logging user/session context is vital for understanding usage patterns and detecting abuse. It supports personalization and enables segment-level metrics (e.g. user cohort performance).
Why it matters: Enables auditing, security review, and lineage in observability dashboards. 

3. Model Name & Version

Different versions (e.g., gpt-3.5-turbo-0613 vs gpt-4o-nightly) behave distinctly. Logging the exact model and timestamp avoids confusion during drift or performance regression.
Why it matters: Critical for debugging behavior changes and comparing performance across upgrades. 

4. Token Usage Per Call (input & output)

Input and output tokens drive cost—and often correlate with latency and performance. Observability platforms like SigNoz and Coralogix emphasize token-level behavior tracking as foundational ([turn0search4]).
Why it matters: Enables real-time cost monitoring and optimization of prompt efficiency.

5. Response Time / Latency

Log the LLM inference latency per call (millisecond resolution), including end-to-end timing from request entry to final output. Sudden spikes indicate infrastructure issues or model throttling.
Why it matters: Enables SLA monitoring and performance regression analysis. 

6. Function or Tool Usage

For applications invoking function calls (e.g. with OpenAI Function Calling or agent tooling), log which functions or agent steps were triggered per prompt.
Why it matters: Offers insight into orchestration paths, helps identify failure domains, and supports structured audit trails. 

7. Temperature and Other Model Parameters

Log generation parameters—temperature, top_p, max_tokens, stop sequences. Changes here alter output behavior.
Why it matters: Ensures reproducibility and performance traceability when tuning or debugging prompts. 

8. Context Length (Prompt Size or RAG Context)

Record the length of prompt context or retrieval chunks used. Knowing when context truncation or overflow happens helps debug omitted or hallucinated content.
Why it matters: Key to diagnosing missing or outdated context leading to hallucination. 

9. Retry Counts and Fallback Logic

If your system retries a request or invokes a secondary model (e.g. smaller model fallback), log the number of retries and what triggered the fallback.
Why it matters: Helps surface systematic failures or cost leakage in cascaded call flows. 

10. Output Delta Metrics (Hallucination Audits)

Capture structured comparison between raw response and expected schema or reference—for instance, missing required fields, unexpected tokens, or semantic drift.
Why it matters: Enables batch analysis of hallucination trends and quantifying reliability over time.

Why You Need These Logs: Observability Research

• Telemetry-aware LLM design (Model Context Protocol) shows real-time metrics and prompt-level traces enable CI and prompt optimization loops ([turn0search13]).

• Tools like Coralogix and TrueFoundry underscore the importance of token-level observability, parameter tracking, and model version tagging to maintain system robustness ([turn0search17]turn0search4]).

• Without these logs, slicing failures by prompt type, user cohort, or model variant becomes impractical—and prompt debugging turns into guesswork.

Example Log Schema

{
  "prompt_id": "a1b2c3",
  "user_id": "user-1234",
  "model": "gpt-4o-0613",
  "temperature": 0.2,
  "token_usage": {
    "input": 75,
    "output": 142
  },
  "latency_ms": 127,
  "function_calls": ["getBalance", "toUpperCase"],
  "context_length": 512,
  "retry_count": 0,
  "hallucination_score": 0.05
}

Each response entry becomes a traceable unit—in analytics, performance dashboards, or audit logs.

Final Takeaway

LLM systems are opaque by default. Without disciplined logging, you lose cost control, risk undetected biases or hallucinations, and make root-cause analysis impossible.

Start logging these ten fields today:
Prompt ID, User ID, Model version, Token usage, Latency, Function usage, Temperature, Context length, Retry/fallback, Output delta metrics.

Together they form the foundation of LLM observability—turning black-box interactions into traceable, auditable, and optimizable workflows.