What Is Temperature?

What Is Temperature?

Temperature in an LLM is an inference setting that controls how sharply or broadly the model samples from next-token probabilities. It belongs to the model family because it changes generation behavior without changing model weights. In FutureAGI production traces, temperature shows up on model calls, prompt experiments, gateway routes, and regression eval runs. Lower values usually make outputs more repeatable; higher values increase variation, which can help ideation but also raise risk for hallucination, schema drift, and inconsistent agent actions.

Why It Matters in Production LLM and Agent Systems

Temperature matters because it controls output variance at the exact point where product guarantees meet probabilistic decoding. If you ignore it, a prompt can pass staging at temperature=0 and fail in production at temperature=0.8 with invalid JSON, inconsistent refusals, looser citations, or tool arguments that vary across identical requests. The failure mode is usually not “bad creativity.” It is a flaky contract between the model and the rest of the system.

Developers feel it first as non-reproducible bugs: the same trace replay produces a different answer, a different tool choice, or a different schema shape. SREs see retries and fallback rate rise when downstream parsers reject outputs. Compliance teams see inconsistent policy language or missing caveats. Product teams see cohort-level drops in task completion, thumbs-down rate, or support escalation rate without a code diff that explains the change.

The risk is sharper for agentic systems. A high-temperature planner step can choose a different retrieval query, a different tool order, or a different memory update, and that early variation can change the whole trajectory. Unlike Ragas faithfulness, which evaluates whether an answer is supported after generation, temperature changes sampling before the answer exists. Treat it as a runtime control that needs eval evidence, not as a harmless creative preference.

How FutureAGI Handles Temperature

Temperature is not a standalone FutureAGI evaluator class, so FutureAGI handles it as experiment metadata attached to model calls, traces, gateway decisions, and eval cohorts. The closest surfaces are fi.client.Client.log, traceAI-openai, Dataset.add_evaluation, and Agent Command Center controls around routing and fallback.

A real workflow looks like this: an engineer is tuning a support agent that answers billing questions and sometimes calls a refund tool. They run the same golden dataset at temperature=0, 0.2, and 0.7, logging each run with the model id, prompt version, route, and temperature tag through fi.client.Client.log. traceAI-openai records the model spans with fields such as gen_ai.request.model, llm.token_count.prompt, llm.token_count.completion, latency, and tool-call metadata. The team then attaches Groundedness, JSONValidation, and TaskCompletion checks through Dataset.add_evaluation.

The engineer does not pick the setting by taste. If temperature=0.7 improves tone but doubles JSON failures, it stays out of the refund path. If temperature=0.2 keeps TaskCompletion stable and reduces repeated canned wording, it can ship behind a small canary. Agent Command Center can then apply traffic-mirroring for comparison, model fallback for provider failures, and a post-guardrail for unsafe or malformed outputs. FutureAGI’s approach is to make temperature a versioned inference variable with trace evidence and eval thresholds, not an invisible SDK default.

How to Measure or Detect Temperature Effects

Measure temperature by comparing controlled cohorts, not by reading one answer:

• Parameter capture: store the temperature value beside gen_ai.request.model, prompt version, route, dataset id, and release cohort.
• Output variance: replay identical inputs multiple times and compare exact match, semantic similarity, schema pass rate, and tool-call agreement.
• Evaluator results: track Groundedness, JSONValidation, and TaskCompletion by temperature bucket; alert on eval-fail-rate-by-cohort.
• Trace and cost signals: watch llm.token_count.prompt, llm.token_count.completion, p99 latency, retry rate, fallback rate, and token-cost-per-trace.
• User-feedback proxies: monitor thumbs-down rate, escalation rate, support reopen rate, and manual review overrides after a temperature change.

Minimal fi.evals check:

from fi.evals import Groundedness

evaluator = Groundedness()
result = evaluator.evaluate(
    response=model_output,
    context=retrieved_context,
)
print(result.score)

Common Mistakes

• Treating temperature as model intelligence. It changes sampling; it does not fix weak retrieval, wrong tools, or missing eval data.
• Testing at one temperature and deploying another. Replay the same golden dataset at production settings before release.
• Raising temperature for irreversible agents. Planner variation can change tool order, parameters, approval paths, or memory writes.
• Combining high temperature with loose schemas. Variation often appears first as invalid JSON, optional-field drift, or enum mismatches.
• Assuming provider settings match. Temperature, top-p, top-k, and defaults are not guaranteed identical across APIs.