> Markdown version of [/videos/2117-why-llms-need-observability-and-how-to-do-it](https://www.wearedevelopers.com/videos/2117-why-llms-need-observability-and-how-to-do-it). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Why LLMs Need Observability and How to Do It

Traditional dashboards lie because a healthy HTTP 200 means nothing if your LLM hallucinates. Learn how to build true AI observability and measure actual output quality.

- **Speakers:** [Rahul Gupta](https://www.wearedevelopers.com/@rahul-gupta)
- **Event:** World Congress 2026 Europe - Virtual Stage
- **Published:** July 3, 2026
- **Duration:** 32:41
- **URL:** https://www.wearedevelopers.com/videos/2117-why-llms-need-observability-and-how-to-do-it

## Summary

The deployment of Large Language Models (LLMs) exposes a fundamental flaw in traditional software monitoring: the "dashboard lie." Standard application performance monitoring might show healthy system status with an HTTP 200 response, even while the AI delivers confident but factually incorrect outputs based on hallucination or flawed context retrieval. Because LLMs are inherently non-deterministic, operations must shift from asking "Is the system up?" to asking "Is the output right?" Closing this blind spot requires treating observability not as an infrastructure check, but as a continuous measurement of AI application quality spanning the entire user interaction.

Since internal model weights operate as a black box, effective LLM observability replies on instrumenting every component *around* the model. By utilizing tools like Langfuse, Datadog, or Arize to capture detailed traces and spans, engineering teams can unearth silent regressions, trace vector search retrieval errors, and track cost or latency creeps. However, telemetry alone only highlights what went wrong. Evaluating when a system is actually fixed requires dedicated "evals"—quantifiable scoring methods ranging from fuzzy string matching to LLM-as-a-judge frameworks and precise human rating. Blending these traces with explicit user ratings and implicit behavioral signals (like query abandonment) builds a highly curated "golden dataset" used to benchmark true system accuracy.

Ultimately, bridging the gap between a broken response and a validated fix establishes a continuous improvement loop: observe, evaluate, debug, and ship. This loop fundamentally redefines application reliability as a shared organizational responsibility. Product managers define the threshold for acceptable quality, domain experts inform correct system logic, customer support flags experiential failures, and engineers build the telemetry to measure it all. Together, this cross-functional rigor transforms fragile AI prototypes into trusted, observable production systems.

**Keywords:** LLM observability techniques, dashboard lie in APM, AI hallucination detection, RAG pipeline tracing, LLM-as-a-judge evaluation, golden dataset curation, silent regression debugging, langfuse trace instrumentation, LLM cost and latency tracking, evals for non-deterministic software, vector search retrieval errors, AI continuous improvement loop, capturing implicit user feedback, cross-functional AI quality assurance

## Chapters

1. **Addressing the gap between prototypes and production** (00:02) — Production usage introduces unpredicted user inputs that deteriorate reliable response quality.
1. **Comparing non-deterministic models to traditional software mechanics** (02:06) — Generative interfaces lack basic deterministic responses and simple binary success mechanisms for straightforward debugging.
1. **Relying on deceptive aggregate health dashboard metrics** (03:31) — Traditional monitoring captures application uptime while completely missing contextually inaccurate outputs displayed to end users.
1. **Recognizing five invisible failure modes of LLMs** (05:47) — Applications silently falter due to generated hallucinations, outdated retrieval documents, quality drift, and latency increases.
1. **Structuring observability for generative model requests** (08:56) — Accurate system monitoring requires recording the entire context environment wrapping model inputs and exact outputs.
1. **Breaking down execution operations into individual trace spans** (11:35) — Segmenting user interactions explains how single requests map into embeddings and required vector search actions.
1. **Selecting crucial operational signals to log during execution** (13:20) — Capturing prompt combinations, retrieval assignment scores, model parameters, and consumed tokens directly guides analytical debugging.
1. **Debugging faulty user interactions using aggregate trace metrics** (15:38) — Examining dips within aggregate quality scoring pinpoints distinct execution failures triggered by outdated retrieval context.
1. **Measuring operational quality with quantitative evaluation datasets** (17:29) — Comparing generated outputs against structured golden datasets standardizes assessment techniques utilizing matched and modeled metrics.
1. **Automating continuous systemic AI response improvement loops** (22:15) — Iteratively observing and experimenting against standard benchmarks establishes vital safety thresholds prior to deploying changes.
1. **Tracing observability across complex multi-step generative architectures** (24:28) — Branching architectural designs need continual measurement covering related embeddings, autonomous agents, and isolated chained operations.
1. **Instrumenting application pipelines utilizing software tracing libraries** (25:49) — Implementing simple library decorators creates automated span measurements isolating precise functional execution durations across programs.
1. **Validating dataset iterations using continuous qualitative user feedback** (27:14) — Aggregating direct manual annotations and inferred application usage patterns continually builds accurate system evaluation datasets.
1. **Distributing AI response quality as a shared organizational responsibility** (28:44) — Safeguarding semantic execution accuracy relies upon strategic cooperation covering engineering branches, specific domain experts, and staff.
1. **Confirming output correctness for responsible generative AI deployment** (31:07) — Prioritizing verifiable contextual accuracy over generalized application uptime reporting continually prevents unforeseen user experience deterioration.

## Related Moments

- [Addressing AI observability and cost transparency with Langfuse](https://www.wearedevelopers.com/videos/100240-analytics-in-the-age-of-agentic-ai-a-tour-of-clickhouse-and-langfuse) (from "Analytics in the Age of Agentic AI: A tour of ClickHouse and Langfuse")
- [Implementing monitoring and observability for AI software deployments](https://www.wearedevelopers.com/videos/1383-the-state-of-genai-machine-learning-in-2025) (from "The State of GenAI & Machine Learning in 2025")
- [Identifying and fixing over-engineered AI calls through observability](https://www.wearedevelopers.com/videos/1465-event-driven-architecture-breaking-conversational-barriers-with-distributed-ai-agents) (from "Event-Driven Architecture: Breaking Conversational Barriers with Distributed AI Agents")
- [Managing observability using natural language AI agents](https://www.wearedevelopers.com/videos/1706-the-ai-ready-stack-rethinking-the-engineering-org-of-the-future) (from "The AI-Ready Stack: Rethinking the Engineering Org of the Future")
- [Introducing LLMs as judges for automated testing](https://www.wearedevelopers.com/videos/100300-testing-ai-agents-automated-evaluation-for-chatbots-rag-systems) (from "Testing AI Agents: Automated Evaluation for Chatbots & RAG Systems")
- [Evaluating and observing large language model performance at scale](https://www.wearedevelopers.com/videos/1010-bringing-the-power-of-ai-to-your-application) (from "Bringing the power of AI to your application.")

## Related Articles

- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)

## Related Jobs

- [Senior AI Agent Software Engineer (Go, Python) (m/f/x)](https://www.wearedevelopers.com/jobs/48277-senior-ai-agent-software-engineer-go-python-m-f-x) at **Dynatrace**
- [Staff Software Engineer, Copilot Experiences](https://www.wearedevelopers.com/jobs/ext/164361-staff-software-engineer-copilot-experiences) at **GitHub**
- [Machine Learning Engineer](https://www.wearedevelopers.com/jobs/ext/588393-machine-learning-engineer) at **Twilio**
- [AI Operations Manager (all genders)](https://www.wearedevelopers.com/jobs/48263-ai-operations-manager-all-genders) at **envelio**
- [Machine Learning Engineer](https://www.wearedevelopers.com/jobs/ext/1355348-machine-learning-engineer) at **TWILIO**
- [AI Software Engineer (Germany)](https://www.wearedevelopers.com/jobs/48317-ai-software-engineer-germany) at **Sunhat**