Let us present “OpenAI Evals Explained with Examples | AI Voice,” a clear walkthrough on evaluating AI models like GPT using real-time data without third-party tools. The video by Jannis Moore from AI Automation demonstrates how to analyze chat completions, track KPIs, and reduce hallucinations directly within OpenAI’s platform.
Join us for practical examples and hands-on tips to streamline AI workflows across voice AI, customer service, and other fields that rely on AI-generated data, showing how in-platform evaluations can make model monitoring faster and more reliable.
Overview of OpenAI Evals
OpenAI Evals is a toolset we can use to measure and monitor the performance of language and voice models directly within the OpenAI platform. It lets us create, run, and track evaluations that reflect our product goals, enabling continuous improvement cycles without exporting data to third-party evaluation systems. By centralizing evals, we streamline feedback loops between production behavior and model tuning.
Purpose and scope of the Evals tool
The primary purpose of Evals is to help us quantify how well a model performs on tasks that matter to our users. The scope includes automated scoring, human-in-the-loop labeling, metric aggregation, and dashboarding for text and voice applications. We can use Evals for unit-style tests (single-turn responses), end-to-end flows (multi-turn chats), and hybrid scenarios like combined ASR + LLM evaluations in voice assistants.
How Evals fits into OpenAI’s platform ecosystem
Evals lives alongside model APIs, fine-tuning pipelines, and other platform features, acting as the measurement layer for model behavior. We integrate Evals with our usage logs and data streams to assess live performance. Because it is embedded in the platform, Evals can leverage the same authentication, telemetry, and compute boundaries we already use, simplifying governance and operational work.
Key benefits of evaluating models in-platform without third-party tools
By running evaluations in-platform, we reduce data transfer overhead and maintain consistent security and privacy controls. We avoid synchronization issues between systems, gain access to native telemetry for latency and usage, and can more rapidly iterate on prompts and policies. This tight coupling shortens the time from detecting an issue to deploying a fix and re-evaluating, which is critical in production environments.
High-level workflow from data ingestion to metric reporting
Our typical workflow begins with ingesting data—historical examples, synthetic tests, or live chat/voice streams—then mapping those examples into eval tasks and expected outputs. We run automated checks, optionally add human labels, compute metrics, and aggregate them into dashboards and alerts. Finally, we feed insights into model prompt adjustments, retrieval augmentations, or fine-tuning, and repeat the cycle.
Core Concepts and Terminology
We want a clear shared vocabulary so teams can design reliable evals and interpret results consistently.
Definition of an eval, task, and example
An eval is a structured evaluation run or suite that groups related tasks and metrics. A task defines the objective and type of interaction (for instance, “classify sentiment” or “answer support queries”), and an example is a single input instance (a user question, audio clip, or chat transcript) paired with expected outcomes or criteria. We build evals from collections of tasks and many examples.
Ground truth, references, and gold labels
Ground truth refers to the authoritative expected output for an example, often created from human judgment or verified sources. References are acceptable answer variants we use in automated scoring (for generation tasks), while gold labels are precise annotations used in classification or retrieval evaluations. We must manage these artifacts carefully to avoid label drift and to represent real-world variability.
Automated vs human-in-the-loop evaluation
Automated evaluation uses deterministic checks and metrics to quickly score many examples; it’s efficient but can miss subtle errors. Human-in-the-loop evaluation involves annotators or raters reviewing outputs for nuance, fairness, or factual correctness. We often combine both: automated filters triage obvious failures while humans review ambiguous cases or label a stratified sample for quality assurance.
Metrics, KPIs, and thresholds explained
Metrics are technical measures (accuracy, F1, latency) that quantify model behavior. KPIs are business-oriented outcomes derived from metrics (e.g., user satisfaction, resolution rate). Thresholds define acceptance criteria or guardrails for deployment. Together, they let us set targets, detect regressions, and drive operational decisions.
Setting Up Evals in OpenAI
We should prepare our account, datasets, and project structures before launching systematic evaluations.
Required permissions and account setup
We need administrative or project-specific permissions to create eval suites, ingest data, and manage human labeling workflows. Our account should have access to the relevant model endpoints and telemetry; we also configure roles for annotators and viewers to ensure secure, auditable evaluation operations.
Project structure and organizing evals
We recommend organizing evals by product area (support bot, voice assistant), by model version, and by evaluation objective. Each project contains eval suites, which in turn contain tasks and example sets. This structure helps us track historical performance per model and per feature, and it makes rollback and comparison simple.
Preparing datasets for evaluation
Datasets should cover representative user scenarios, including edge cases and failure modes. We split data into development (for iterative testing) and holdout sets (for objective reporting). For voice, datasets include raw audio, transcriptions, and aligned timestamps; for chat, include multi-turn context, user metadata, and system actions. We also tag examples with difficulty or priority to steer human review.
Sample API call structure and where to place prompts
When we call an eval-enabled API or construct an eval object, we typically supply: metadata, model identifiers, prompt templates, example inputs, expected outputs, and scoring rules. A simple structure looks like this (pseudo-JSON for clarity):
{ “eval_name”: “support_resolution_v1”, “model”: “gpt-4o-mini”, “tasks”: [ { “task_type”: “chat_resolution”, “prompt_template”: “System: You are a support assistant. User: {{ user_message }}”, “examples”: [ { “input”: {“user_message”: “My account is locked.”}, “expected”: {“resolution”: “provide_unlock_steps”, “confidence_threshold”: 0.8} } ], “scoring”: {“rule_type”: “classification”, “labels”: [“resolved”,”escalate”]} } ] }
We place prompts in prompt_template fields and keep example-specific context in example inputs so the eval engine can instantiate prompts per example. Scoring rules reference expected outputs or gold labels.
Designing Evaluation Tasks
Good tasks mirror product goals and produce actionable signals.
Selecting evaluation objectives aligned with product goals
We start by mapping user journeys to measurable objectives: Does the chat bot resolve issues? Does the voice assistant retrieve correct facts? Each eval objective should translate to one or more metrics that impact our KPIs, and we prioritize tasks that affect revenue, safety, or user retention.
Crafting prompts and instructions for consistent model behavior
We standardize instructions and few-shot context so that evaluations measure model capability, not prompt variability. Our prompts should fix system roles, clarify expected output formats, and include safety instructions. We version prompts and use control examples to detect prompt-induced changes.
Types of tasks: classification, generation, summarization, instruction-following
We categorize tasks by output type: classification (intent detection, sentiment), generation (free-form answers), summarization (condensing text), and instruction-following (perform a step-by-step task). Each type has specialized scoring: classification uses labels and confusion matrices, generation uses overlap and semantic metrics, and instruction-following uses compliance and step-count checks.
Handling multi-turn chat completions and context windows
Multi-turn evals include full chat histories and may require stateful scoring (did the assistant reach resolution by turn N?). We manage context windows carefully: provide representative context lengths and simulate truncated contexts to test robustness. For long histories, we may compress or summarize earlier turns to fit model context limits while preserving critical state.
Evaluation Metrics and KPIs
We choose metrics that are interpretable and tied to user value.
Common metrics for text: accuracy, F1, BLEU, ROUGE, perplexity and their use cases
Accuracy and F1 suit classification tasks, with F1 preferable on imbalanced classes. BLEU and ROUGE help compare generated text to references (useful in summarization and translation) but can miss semantic equivalence. Perplexity measures model confidence and fluency but doesn’t map directly to user satisfaction. We combine these metrics where appropriate to get a fuller picture.
Voice-specific metrics: WER, CER, MOS, latency
For voice pipelines, Word Error Rate (WER) and Character Error Rate (CER) quantify ASR performance. Mean Opinion Score (MOS) captures perceived audio quality (often collected via human raters). Latency measures end-to-end response time, which is crucial for real-time voice assistants. We track these alongside downstream LLM metrics to measure joint system performance.
Business KPIs: user satisfaction, error rate, escalation rate, time-to-resolution
Business KPIs translate model metrics into outcomes we care about: user satisfaction surveys, rate of incorrect answers, fraction of interactions escalated to humans, and average time to resolution. We use these KPIs to prioritize fixes and to evaluate A/B tests in the context of user impact.
Choosing thresholds, confidence bands, and acceptance criteria
We set thresholds based on historical baselines, user tolerance, and safety needs. Confidence bands (e.g., 95% intervals) help determine statistical significance for changes. Acceptance criteria should be actionable and include both absolute targets and relative improvement goals to guide iteration.
Reducing and Measuring Hallucinations
Hallucinations are a critical failure mode, and we need clear processes to detect and reduce them.
Defining hallucinations in LLM outputs
We define hallucinations as generated statements that are not supported by the prompt, known facts, or retrieval sources and that present false information as true. This includes fabricated citations, invented dates, or incorrect factual claims presented confidently.
Detection strategies: rule-based checks, fact verification, retrieval-augmented comparisons
Detection starts with simple heuristics (presence of uncertain date formats, unsupported numeric claims) and advances to fact verification: cross-checking claims against trusted knowledge bases or using retrieval-augmented pipelines that compare the model output to retrieved documents. We also use entailment models to verify whether the output is supported by source passages.
Scoring and labelling hallucinations within eval datasets
We annotate examples with hallucination labels and severity (minor, major, critical). Scoring can be binary (hallucinated or not) or graded by risk. We reserve a sample of outputs for human review to calibrate automated detectors and to build training data for better classifiers.
Mitigation techniques: prompt engineering, constrained generation, retrieval augmentation
Mitigations include prompt tactics (ask the model to cite sources, require uncertainty statements), constrained decoding (reduce creative sampling for factual tasks), and retrieval augmentation (supply verified documents as context). We also implement fallback behaviors: when confidence is low or verification fails, the model should decline to answer or escalate to a human.
Real-time Data and Streaming Evaluations
Evaluations should reflect live behavior, and streaming approaches let us respond faster.
Ingesting live chat completion data for near-real-time evals
We pipe production chat completions into eval pipelines with privacy safeguards. We sample or aggregate enough data to detect trends without overwhelming annotation queues. Real-time ingestion allows us to run periodic checks and to trigger alerts for anomalies such as sudden spikes in errors or latency.
Streaming metrics and how to compute them incrementally
We compute streaming metrics by maintaining running aggregates and sliding windows—e.g., last-hour WER, last 10,000 chats accuracy. Incremental computation reduces latency in metric updates and supports real-time dashboards. We ensure that statistical estimators are stable and correct for skew and variance.
Latency considerations and event-driven evaluation triggers
We measure both processing latency and user-observed latency. Event-driven triggers kick off deeper evaluation workflows when thresholds are exceeded (e.g., burst in hallucination rate), enabling rapid human review or automated mitigations. We architect pipelines to ensure triggers execute within acceptable operational windows.
Handling noisy or partial data and methods for smoothing
Production data is noisy: partial transcripts, interrupted audio, and incomplete sessions. We apply smoothing techniques like exponential moving averages, robust statistics (median, trimmed means), and backfill strategies for delayed labels. We also tag events with data quality flags so downstream metrics can adjust for incomplete inputs.
Voice AI Specific Evaluation Example
We often need to evaluate the combined performance of ASR and LLM components in voice systems.
Setting up audio capture, transcription, and alignment for voice data
We capture raw audio with metadata (device, sample rate, timestamps), transcribe using ASR systems, and store both audio and transcripts. Alignment maps transcript tokens to audio timestamps so we can analyze where errors occur and correlate audio artifacts with downstream failures.
Combining ASR outputs with LLM responses for joint evaluation
We create joint examples that pair ASR outputs with the LLM’s response and a gold label for the end-to-end goal (e.g., correct action taken). This lets us analyze root causes: was a wrong action due to misrecognition or a hallucination? Joint evals use composite metrics that track both ASR accuracy and LLM correctness.
Measuring perceived quality: MOS collection and automated proxies
We collect MOS scores from human raters for perceived audio and response quality. For scalable proxies, we use metrics like WER, ASR confidence, dialogue coherence scores, and response time. We correlate automatic proxies with MOS to validate their effectiveness.
Example evaluation scenario: voice assistant answer accuracy and naturalness
In a typical scenario, we feed recorded user queries through ASR, pass the transcript plus relevant context to the LLM, and evaluate the final spoken or synthesized response. We check if the assistant provided a correct answer (accuracy), whether the phrasing felt natural (MOS or proxy), and whether latency met our real-time SLA. Failures are traced back to either the ASR or the LLM, guiding targeted improvements.
Practical Examples and Walkthroughs
We illustrate end-to-end procedures for common evaluation needs.
Example 1: Evaluating a customer support chat model for correct resolution
We assemble a dataset of resolved support tickets and representative user messages. Our task checks whether the model’s final response maps to the correct resolution category. We compute resolution accuracy, escalation rate, and average turns-to-resolution. We triage failures by frequency and severity, prioritize fixes (prompt changes, retrieval tuning), and re-run the eval on a holdout set.
Example 2: Measuring hallucination rate on knowledge-base driven Q&A
We craft QA pairs from the knowledge base and run the model with and without retrieval augmentation. We use automated fact-checkers and human raters to label hallucinations, computing hallucination rate per question type. We compare baseline and retrieval-augmented systems, inspect cases where retrieval returned no evidence, and tune retrieval relevance or answer grounding.
Example 3: A/B testing two prompt templates and comparing KPIs
We design two prompt templates and route live traffic or sampled data to both variants. We measure core KPIs (correctness, latency, user satisfaction) and technical metrics (token usage, perplexity). We compute confidence intervals to assess statistical significance and choose the prompt that meets our acceptance criteria. We also verify no safety regressions arose in either variant.
Step-by-step: from dataset to result dashboard for each example
Our steps are: (1) define objective and metrics, (2) gather representative dataset and gold labels, (3) design task(s) and prompt templates, (4) run evals (automated and human-in-the-loop), (5) compute metrics and visualize in dashboards, (6) analyze failures and categorize root causes, (7) implement fixes, and (8) re-evaluate. We automate this loop as much as possible to maintain rapid iteration.
Conclusion
We can make model evaluation an integrated, continuous practice that drives product quality and user trust.
Recap of why in-platform evaluation is powerful for voice and chat use cases
In-platform evals reduce friction, tighten data and control boundaries, and allow us to measure end-to-end experiences across ASR and LLM components. This is especially valuable for voice and chat use cases where latency, context, and multimodal signals matter.
Key takeaways: metrics, workflows, and continuous improvement loops
We should align metrics to business KPIs, design tasks that reflect real user journeys, combine automated and human evaluations, and close the loop by feeding insights back into prompts, retrieval, or model training. Streaming and real-time evals help detect regressions quickly.
Practical next actions to start evaluating models with OpenAI Evals
We recommend: define high-impact eval objectives, assemble representative datasets and gold labels, set up a project and permission model, create initial eval tasks, and run baseline comparisons across model versions. Start small, iterate, and expand coverage as you gain confidence.
Encouragement to iterate, measure, and align evaluations with business goals
We encourage us to treat evaluation as an ongoing engineering discipline: iterate prompts, measure outcomes, and align every eval with a clear business impact. By doing so, we will improve reliability, reduce hallucinations, and deliver better user experiences across voice and chat products.
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