Evaluation for LLM Applications

Contrast intrinsic evaluations, using perplexity and benchmark accuracy, with extrinsic evaluations that test real-world task performance in applications like customer support chatbots.

Explore what makes a large language model good by balancing accuracy, helpfulness, safety, and latency, and learn how to evaluate LLMs across multiple criteria.

Explore the core challenges in evaluating generative outputs from large language models, including subjectivity, open-endedness, context sensitivity, and scalability, with insights on human and automated evaluation methods.

Log inputs, outputs, and metadata to create a complete trace of each interaction—capturing prompts, system instructions, context documents, parameters, generated text, confidence, and latency for reproducible error analysis.

Set up observability pipelines with OpenTelemetry, Prometheus, and Grafana to monitor latency, token usage, and errors in production. Enable real-time alerts and dashboards to detect anomalies and ensure model performance.

Track latency, token usage, and user satisfaction to monitor large language models in production, linking speed, cost, and quality with error rates and safety checks.

Explore the three main failure categories of LLMs—hallucinations, bias, and toxicity—and learn targeted mitigation strategies like grounding with retrieval, data curation, fairness checks, and safety filters.

Learn root cause analysis frameworks for evaluating large language models, using five whys, fishbone diagrams, and fault tree analysis to diagnose causes, fix errors, and prevent recurrence.

Implement feedback loops that capture explicit and implicit user signals to drive continuous improvement, tag errors by category, and close the loop by logging traces including input, output, and metadata.

Compare human and automatic evaluation for large language models, highlighting nuance, context, and safety versus fast, scalable metrics like blue and rouge. Combine both methods to balance depth and efficiency.

Explore the law-as-a-judge method, where one LLM evaluates another’s outputs, scoring and ranking for accuracy, clarity, and helpfulness, enabling scalable, cost-effective evaluation with human spot checks.

Compare pairwise outputs to select the clearer, more accurate result, then score outputs across dimensions like accuracy, clarity, safety, and helpfulness.

Discover how retrieval augmented generation differs from plain language models by using a retriever and a generator that pull external sources to deliver up-to-date, factual answers and reduce hallucinations.

Evaluate retrieval quality in retrieval augmented generation by measuring recall, precision, and relevance to produce factual, grounded, and trustworthy answers, avoiding hallucinations from incomplete context.

Assess retrieval quality in retrieval augmented generation by examining recall, precision, and relevance to ensure factual, grounded, and trustworthy answers.

Design evaluation in production environments with continuous logging, real-time monitoring, and user feedback to measure real-world performance, safety, and privacy while balancing cost and latency.

Integrate automated evaluation into CI/CD pipelines with pre-deployment checks, latency and token usage metrics, safety thresholds, and canary deployments, monitored by Prometheus or Grafana.

Set actionable alerting thresholds for latency, token usage, and toxicity, and implement a clear incident response playbook with ownership and escalation to minimize downtime and protect user trust.

Learn how to design scalable human-in-the-loop review systems that combine sampling, triage, and hybrid automation to ensure nuanced evaluation beyond automated metrics.

Balance evaluation quality with budget constraints by comparing human review, automated checks, and hybrid approaches, using sampling and tiered evaluation to optimize resources and outcomes.

Explore token efficiency and model selection to reduce costs by designing shorter prompts, truncating conversations, and using hybrid routing with retrieval augmented generation.

Review intrinsic evaluation, including perplexity, to balance accuracy, helpfulness, safety, and latency. Connect instrumentation, error analysis, LM judge approaches, recall, precision, relevance, rag systems, and production monitoring for evaluation.