Humanloop developer on retainer: prompt version fetched once at FastAPI startup — Kubernetes pods not restarted on Humanloop prompt update — production serves stale v7 prompt for 12 days after v8 deployed — 2,400 documents processed with wrong parameters — Humanloop developer on monthly retainer

humanloop 0.9.4 • Python 3.11 • FastAPI 0.111.0 • GKE 1.29 • 4-replica deployment • prompt v7 cached at startup September 28 • prompt v8 deployed Humanloop dashboard October 5 • pods never restarted • v7 active 12 days • max_tokens=500 vs v8’s max_tokens=1500 • 2,400 documents with stale prompt • fix: asyncio polling every 5 minutes • prompt updates within 5 minutes • 3.0h retainer engagement

Marlowe Digital Solutions Ltd. builds an AI-powered legal document summarization service for law firms and corporate legal teams. The service accepts uploaded contracts and produces structured summaries highlighting key clauses, obligations, and risk factors. They use Humanloop to manage their summarization prompt, which undergoes regular refinement based on feedback from attorney reviewers. On October 5, 2026, their legal technology team deployed prompt version v8 via the Humanloop dashboard — a significant update that raised max_tokens from 500 to 1,500 and added an explicit instruction to include all liability clauses and indemnification provisions in every summary. Twelve days later, on October 17, their QA engineer ran a spot check on recent summaries and noticed that the v8 changes were not reflected in production output. The summaries were still short and still omitting liability clauses. They brought in a Humanloop retainer developer to explain why a prompt update deployed through Humanloop’s dashboard had not taken effect in their production service. The answer was in the FastAPI startup code.

Their application used FastAPI’s lifespan() context manager to initialize the Humanloop client and fetch the production prompt at startup. The fetched prompt version was stored in a module-level variable shared across all request handlers:

# app/main.py
# Marlowe Digital Solutions Ltd.
# humanloop 0.9.4, Python 3.11, FastAPI 0.111.0

import os
import logging
from contextlib import asynccontextmanager
from humanloop import Humanloop
import fastapi

logger = logging.getLogger(__name__)

hl = Humanloop(api_key=os.environ["HUMANLOOP_API_KEY"])
PROMPT_ID = "prm_legal_summarizer"

# Module-level variable — fetched once at startup, never refreshed
active_prompt = None

@asynccontextmanager
async def lifespan(app: fastapi.FastAPI):
    global active_prompt

    # Fetch the currently deployed prompt version for the production environment
    prompt = hl.prompts.get(id=PROMPT_ID, environment="production")
    active_prompt = prompt
    logger.info(f"Loaded Humanloop prompt version: {prompt.version_id}")

    yield  # Application runs here — active_prompt never updated

    logger.info("Shutting down prompt manager")

app = fastapi.FastAPI(lifespan=lifespan)


# app/processor.py
async def summarize_document(document_text: str) -> str:
    """Summarize a legal document using the currently active prompt version."""

    response = openai_client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {
                "role": "system",
                "content": active_prompt.template,  # v7 template for 12 days
            },
            {
                "role": "user",
                "content": f"Summarize the following legal document:\n\n{document_text}",
            },
        ],
        max_tokens=active_prompt.parameters.max_tokens,  # 500 in v7, 1500 in v8
        temperature=active_prompt.parameters.temperature,  # 0.7 in v7, 0.3 in v8
    )
    return response.choices[0].message.content

The GKE deployment ran four replicas of the application. All four pods had last restarted on September 28, 2026 — the date of the previous code deployment — when each pod had fetched prompt v7 at startup and logged “Loaded Humanloop prompt version: prmv_7a3b2c1d”. On October 5, when the legal technology team deployed prompt v8 via the Humanloop dashboard, no code change was committed and no new container image was built. The Humanloop deployment updated the environment record in Humanloop’s backend — now hl.prompts.get(id=PROMPT_ID, environment='production') would return v8 — but the running pods never called that method again. All four pods continued serving the v7 template and parameters from their startup-time cache.

The team had adopted Humanloop specifically to enable prompt updates without code deploys. The implicit assumption was that updating the “production” environment in Humanloop would make the new prompt version active in production. That assumption was correct for any new pod that started after the deployment — but incorrect for pods that were already running. The mismatch between the Humanloop deployment model (“deploy a version to an environment”) and the application’s initialization model (“read the environment’s current version once at startup”) was the root cause.

The retainer developer confirmed the diagnosis by querying the pod logs across all four replicas:

# diagnosis: check which prompt version each pod loaded at startup
# kubectl logs against all 4 replicas

$ kubectl logs -l app=legal-summarizer --since=720h | grep "Loaded Humanloop"

pod/legal-summarizer-7b9c4f-xk2p1: Loaded Humanloop prompt version: prmv_7a3b2c1d  (2026-09-28 11:42:07 UTC)
pod/legal-summarizer-7b9c4f-mn8q3: Loaded Humanloop prompt version: prmv_7a3b2c1d  (2026-09-28 11:42:09 UTC)
pod/legal-summarizer-7b9c4f-rs5t6: Loaded Humanloop prompt version: prmv_7a3b2c1d  (2026-09-28 11:42:11 UTC)
pod/legal-summarizer-7b9c4f-vw2y4: Loaded Humanloop prompt version: prmv_7a3b2c1d  (2026-09-28 11:42:13 UTC)

# All 4 pods loaded prmv_7a3b2c1d (v7) on September 28.
# No "Loaded Humanloop prompt version: prmv_8e5f6a7b" (v8) anywhere.
# Humanloop dashboard confirms prmv_8e5f6a7b deployed to production October 5.

$ python -c "
from humanloop import Humanloop
hl = Humanloop(api_key='...')
p = hl.prompts.get(id='prm_legal_summarizer', environment='production')
print(f'Current production version: {p.version_id}')
print(f'max_tokens: {p.parameters.max_tokens}')
print(f'temperature: {p.parameters.temperature}')
"
# Current production version: prmv_8e5f6a7b
# max_tokens: 1500
# temperature: 0.3

# Humanloop says v8. All 4 pods serve v7.
# Gap: 12 days since October 5 deployment.

The fix implemented an asyncio background task that polls Humanloop every five minutes and reloads active_prompt if the version ID has changed:

# app/main.py — fixed version
import asyncio
import logging
import os
from contextlib import asynccontextmanager
from humanloop import Humanloop
import fastapi

logger = logging.getLogger(__name__)

hl = Humanloop(api_key=os.environ["HUMANLOOP_API_KEY"])
PROMPT_ID = "prm_legal_summarizer"
PROMPT_REFRESH_INTERVAL_SECONDS = 300  # 5 minutes

active_prompt = None


async def refresh_prompt_periodically(interval: int) -> None:
    """
    Poll Humanloop every `interval` seconds.
    Reload active_prompt if the deployed version ID has changed.
    Exceptions are caught and logged — a failed refresh attempt
    retains the last known good version rather than crashing.
    """
    global active_prompt
    while True:
        await asyncio.sleep(interval)
        try:
            prompt = hl.prompts.get(id=PROMPT_ID, environment="production")
            if active_prompt is None or prompt.version_id != active_prompt.version_id:
                old_version = active_prompt.version_id if active_prompt else "none"
                active_prompt = prompt
                logger.info(
                    f"Prompt refreshed: {old_version} → {prompt.version_id} "
                    f"(max_tokens={prompt.parameters.max_tokens}, "
                    f"temperature={prompt.parameters.temperature})"
                )
        except Exception as e:
            logger.warning(
                f"Prompt refresh failed (retaining {active_prompt.version_id}): {e}"
            )


@asynccontextmanager
async def lifespan(app: fastapi.FastAPI):
    global active_prompt

    # Fetch prompt at startup
    prompt = hl.prompts.get(id=PROMPT_ID, environment="production")
    active_prompt = prompt
    logger.info(f"Loaded Humanloop prompt version: {prompt.version_id}")

    # Start background polling task
    refresh_task = asyncio.create_task(
        refresh_prompt_periodically(PROMPT_REFRESH_INTERVAL_SECONDS)
    )

    yield

    # Graceful shutdown: cancel polling task
    refresh_task.cancel()
    try:
        await refresh_task
    except asyncio.CancelledError:
        pass
    logger.info("Prompt refresh task stopped")


app = fastapi.FastAPI(lifespan=lifespan)

After deploying the fix on October 17, all four pods picked up prompt v8 within five minutes of the polling task starting — the next poll cycle returned prmv_8e5f6a7b, detected the version ID change, and reloaded active_prompt. Each pod logged “Prompt refreshed: prmv_7a3b2c1d → prmv_8e5f6a7b (max_tokens=1500, temperature=0.3)”. Documents processed from that point forward used the v8 parameters. The legal technology team verified the fix by submitting a test document with multiple liability clauses and confirming the summary included them in full.

The impact assessment covered the 12-day window from October 5 to October 17. The service processes approximately 200 documents per business day, totaling roughly 2,400 documents processed with the v7 prompt parameters. The primary consequence of v7 vs v8 was in summary completeness: v7’s max_tokens=500 constraint caused the model to cut off before covering all document sections, and the absence of the explicit liability clause instruction meant those clauses were often omitted from the summary structure. An attorney review of a random sample of 50 documents from the 12-day window identified 3 documents where the missing liability clause coverage could have caused a material misunderstanding by the attorney using the summary as a reference for a contract review. Those 3 documents were re-summarized using v8 and re-delivered to the relevant attorneys.

The retainer developer also evaluated whether the alternative approach — calling humanloop_client.prompts.call() directly per request instead of prompts.get() at startup — was appropriate for this use case. The prompts.call() method fetches the current deployed version and calls the LLM in a single API call, meaning every document summarization request would automatically use the current Humanloop production version without any caching or polling. The trade-off is latency: every request incurs a Humanloop API call in addition to the OpenAI call, adding approximately 80ms to 120ms of overhead. For a document summarization workload where the OpenAI call itself takes 3,000ms to 8,000ms, this overhead is negligible. The retainer developer recommended migrating to prompts.call() as the long-term solution and keeping the polling mechanism as a migration step. The team chose polling because it avoided refactoring the prompt parameter handling code immediately and allowed a staged migration.

The postmortem defined a new rule in the engineering handbook: any application that reads from Humanloop using prompts.get() must either refresh the prompt periodically (polling or webhook), call prompts.call() per request, or explicitly document that the application only picks up new Humanloop versions on pod restart, with a matching Humanloop deployment runbook step that includes a rolling restart. The prompt deployment verification step was added to the legal technology team’s post-deployment checklist: after deploying a new version to the production environment in Humanloop, confirm within ten minutes that at least one production pod log shows the new version ID, either from a startup log or from a polling-refresh log. If the new version does not appear within the polling interval, investigate immediately.

Total retainer engagement: 3.0 hours (0.5h initial triage: review Humanloop dashboard version history, confirm v8 deployed October 5, check production pod startup logs via kubectl, confirm all 4 pods show prmv_7a3b2c1d from September 28 with no subsequent reload; 0.75h root cause: examine FastAPI lifespan() code, confirm prompts.get() is called only at startup with no subsequent refresh, document the gap between Humanloop deployment model and application caching model, estimate 12-day stale window and 2,400 affected documents; 1.0h fix implementation: write refresh_prompt_periodically() async polling function with exception handling, update lifespan() to start polling task and cancel on shutdown, add version-change detection logic, add structured logging for version transitions, deploy to GKE, verify v8 pickup within 5 minutes on all 4 pods; 0.75h validation and documentation: re-process 50 sampled v7-window documents through v8 for quality comparison, identify 3 documents requiring re-delivery, write postmortem with root cause and new prompt deployment runbook section).

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